essay on online learning during pandemic

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

140k Accesses

217 Citations

337 Altmetric

Metrics details

• Science, technology and society

The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

Mackey, J., Gilmore, F., Dabner, N., Breeze, D. & Buckley, P. J. Online Learn. Teach. 8 , 35–48 (2012).

Google Scholar  

Sands, T. & Shushok, F. The COVID-19 higher education shove. Educause Review https://go.nature.com/3o2vHbX (16 October 2020).

Hodges, C., Moore, S., Lockee, B., Trust, T. & Bond, M. A. The difference between emergency remote teaching and online learning. Educause Review https://go.nature.com/38084Lh (27 March 2020).

Beatty, B. J. (ed.) Hybrid-Flexible Course Design Ch. 1.4 https://go.nature.com/3o6Sjb2 (EdTech Books, 2019).

Skinner, B. F. Science 128 , 969–977 (1958).

Article   Google Scholar  

Keller, F. S. J. Appl. Behav. Anal. 1 , 79–89 (1968).

Darling-Hammond, L. et al. Restarting and Reinventing School: Learning in the Time of COVID and Beyond (Learning Policy Institute, 2020).

Fulton, C. Information Learn. Sci . 121 , 579–585 (2020).

Pennisi, E. Science 369 , 239–240 (2020).

Silva, E. & White, T. Change The Magazine Higher Learn. 47 , 68–72 (2015).

McIsaac, M. S. & Gunawardena, C. N. in Handbook of Research for Educational Communications and Technology (ed. Jonassen, D. H.) Ch. 13 (Simon & Schuster Macmillan, 1996).

Irvine, V. The landscape of merging modalities. Educause Review https://go.nature.com/2MjiBc9 (26 October 2020).

Stein, J. & Graham, C. Essentials for Blended Learning Ch. 1 (Routledge, 2020).

Maloy, R. W., Trust, T. & Edwards, S. A. Variety is the spice of remote learning. Medium https://go.nature.com/34Y1NxI (24 August 2020).

Lockee, B. J. Appl. Instructional Des . https://go.nature.com/3b0ddoC (2020).

Dunlap, J. & Lowenthal, P. Open Praxis 10 , 79–89 (2018).

Johnson, N., Veletsianos, G. & Seaman, J. Online Learn. 24 , 6–21 (2020).

Vaughan, N. D., Cleveland-Innes, M. & Garrison, D. R. Assessment in Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry (Athabasca Univ. Press, 2013).

Conrad, D. & Openo, J. Assessment Strategies for Online Learning: Engagement and Authenticity (Athabasca Univ. Press, 2018).

Download references

Author information

Authors and affiliations.

School of Education, Virginia Tech, Blacksburg, VA, USA

Barbara B. Lockee

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Barbara B. Lockee .

Ethics declarations

Competing interests.

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Lockee, B.B. Online education in the post-COVID era. Nat Electron 4 , 5–6 (2021). https://doi.org/10.1038/s41928-020-00534-0

Download citation

Published : 25 January 2021

Issue Date : January 2021

DOI : https://doi.org/10.1038/s41928-020-00534-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A comparative study on the effectiveness of online and in-class team-based learning on student performance and perceptions in virtual simulation experiments.

BMC Medical Education (2024)

Leveraging privacy profiles to empower users in the digital society

• Davide Di Ruscio
• Paola Inverardi
• Phuong T. Nguyen

Automated Software Engineering (2024)

Growth mindset and social comparison effects in a peer virtual learning environment

• Pamela Sheffler
• Cecilia S. Cheung

Social Psychology of Education (2024)

Nursing students’ learning flow, self-efficacy and satisfaction in virtual clinical simulation and clinical case seminar

• Sunghee H. Tak

BMC Nursing (2023)

Online learning for WHO priority diseases with pandemic potential: evidence from existing courses and preparing for Disease X

• Heini Utunen
• Corentin Piroux

Archives of Public Health (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Online Learning During the Pandemic

Today’s rapid shift in the traditional patterns of social lifestyle caused by the COVID-19 pandemic outbreak has resulted in the necessity to define possible approaches to living a full-scale life while respecting the need for social distancing. Thus, one of the major challenges in the context was to define the patterns of work and education process during the global lockdown. When it comes to the notion of education, the process of online learning has become a salvation to the problem of education access and efficiency. The definition of online learning stands for an umbrella term that encompasses a series of machine-learning techniques that allow learners to acquire relevant knowledge with the help of technology in a certain sequence [1]. Although the process of online learning has become widely popular due to an ongoing emergency, the term genesis can be traced back to decades prior to COVID-19, as machine learning is also regarded as a scientific outbreak besides being an urgent problem solution [2]. Thus, once the necessity of technological intervention in education became an absolute necessity, there had already been a variety of devices and software applications to implement.

Over the times of the pandemic, the concept of educational technology (EdTech) has become widely popular with software developers and investors. In fact, EdTech, despite a relatively long existence in the market, has now introduced a variety of software applications like Classplus and Edmingle that would facilitate the process of education in both developing and developed countries [3]. Moreover, the already existing educational sources powered by Microsoft and Google are also of great efficiency for today’s learners, as their plain yet efficient design helps students accommodate quickly to the process. Hence, taking everything into consideration, it might be concluded that the process for online education that was rapidly facilitated by a pandemic outbreak is likely to develop greatly over the next few years, creating a full-scale competition for conventional patterns of learning.

S. C. H. Hoi, D. Sahoo, J. Lu, and P. Zhao. “Online learning: A comprehensive survey,” SMU Technical Report , vol. 1, pp. 1-100, 2018.

A. Muhammad, and K. Anwar. “Online learning amid the COVID-19 pandemic: Students’ perspectives.” Online Submission , vol. 2, no. 1, pp. 45-51, 2020.

D. Shivangi. “Online learning: A panacea in the time of COVID-19 crisis.” Journal of Educational Technology Systems , vol. 49, no.1, pp. 5-22, 2020.

Cite this paper

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2022, February 16). Online Learning During the Pandemic. https://studycorgi.com/online-learning-during-the-pandemic/

"Online Learning During the Pandemic." StudyCorgi , 16 Feb. 2022, studycorgi.com/online-learning-during-the-pandemic/.

StudyCorgi . (2022) 'Online Learning During the Pandemic'. 16 February.

1. StudyCorgi . "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

Bibliography

StudyCorgi . "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

StudyCorgi . 2022. "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

This paper, “Online Learning During the Pandemic”, was written and voluntary submitted to our free essay database by a straight-A student. Please ensure you properly reference the paper if you're using it to write your assignment.

Before publication, the StudyCorgi editorial team proofread and checked the paper to make sure it meets the highest standards in terms of grammar, punctuation, style, fact accuracy, copyright issues, and inclusive language. Last updated: February 16, 2022 .

If you are the author of this paper and no longer wish to have it published on StudyCorgi, request the removal . Please use the “ Donate your paper ” form to submit an essay.

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

• View Article
• Google Scholar
• PubMed/NCBI
• 30. Serrat O. Social network analysis. Knowledge solutions: Springer; 2017. p. 39–43. https://doi.org/10.1007/978-981-10-0983-9_9
• 33. Rong Y, Xu E, editors. Strategies for the Management of the Government Affairs Microblogs in China Based on the SNA of Fifty Government Affairs Microblogs in Beijing. 14th International Conference on Service Systems and Service Management 2017.
• 34. Hu X, Chu S, editors. A comparison on using social media in a professional experience course. International Conference on Social Media and Society; 2013.
• 35. Bydžovská H. A Comparative Analysis of Techniques for Predicting Student Performance. Proceedings of the 9th International Conference on Educational Data Mining; Raleigh, NC, USA: International Educational Data Mining Society2016. p. 306–311.
• 40. Olivares D, Adesope O, Hundhausen C, et al., editors. Using social network analysis to measure the effect of learning analytics in computing education. 19th IEEE International Conference on Advanced Learning Technologies 2019.
• 41. Travers J, Milgram S. An experimental study of the small world problem. Social Networks: Elsevier; 1977. p. 179–197. https://doi.org/10.1016/B978-0-12-442450-0.50018–3
• 43. Okamoto K, Chen W, Li X-Y, editors. Ranking of closeness centrality for large-scale social networks. International workshop on frontiers in algorithmics; 2008; Springer, Berlin, Heidelberg: Springer.
• 47. Ding Y, Yang X, Zheng Y, editors. COVID-19’s Effects on the Scope, Effectiveness, and Roles of Teachers in Online Learning Based on Social Network Analysis: A Case Study. International Conference on Blended Learning; 2021: Springer.
• 64. Boys C, Brennan J., Henkel M., Kirkland J., Kogan M., Youl P. Higher Education and Preparation for Work. Jessica Kingsley Publishers. 1988. https://doi.org/10.1080/03075079612331381467

Advertisement

Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

Cite this article

• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

937k Accesses

241 Citations

4 Altmetric

Explore all metrics

Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

Avoid common mistakes on your manuscript.

1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies, 26 (2), 1665–1685.

Article   Google Scholar  

Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25 , 5261–5280.

Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures, 11 (3), 173–190.

Barrot, J. S. (2016). Using Facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29 (3), 286–301.

Barrot, J. S. (2018). Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from 2010 to 2017. Journal of Computer Assisted Learning, 34 (6), 863–875.

Barrot, J. S. (2020). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research .  https://doi.org/10.1177/0735633120972010 .

Barrot, J. S. (2021). Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer Assisted Language Learning . https://doi.org/10.1080/09588221.2021.1883673 .

Bergen, N., & Labonté, R. (2020). “Everything is perfect, and we have no problems”: Detecting and limiting social desirability bias in qualitative research. Qualitative Health Research, 30 (5), 783–792.

Birks, M., & Mills, J. (2011). Grounded theory: A practical guide . Sage.

Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22 , 1–18.

Buehler, M. A. (2004). Where is the library in course management software? Journal of Library Administration, 41 (1–2), 75–84.

Carter, R. A., Jr., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences, 121 (5/6), 321–329.

Cavanaugh, C. S., Barbour, M. K., & Clark, T. (2009). Research and practice in K-12 online learning: A review of open access literature. The International Review of Research in Open and Distributed Learning, 10 (1), 1–22.

Cicchetti, D., Bronen, R., Spencer, S., Haut, S., Berg, A., Oliver, P., & Tyrer, P. (2006). Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. The Journal of Nervous and Mental Disease, 194 (8), 557–564.

Copeland, W. E., McGinnis, E., Bai, Y., Adams, Z., Nardone, H., Devadanam, V., & Hudziak, J. J. (2021). Impact of COVID-19 pandemic on college student mental health and wellness. Journal of the American Academy of Child & Adolescent Psychiatry, 60 (1), 134–141.

Day, T., Chang, I. C. C., Chung, C. K. L., Doolittle, W. E., Housel, J., & McDaniel, P. N. (2021). The immediate impact of COVID-19 on postsecondary teaching and learning. The Professional Geographer, 73 (1), 1–13.

Donitsa-Schmidt, S., & Ramot, R. (2020). Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching, 46 (4), 586–595.

Drane, C., Vernon, L., & O’Shea, S. (2020). The impact of ‘learning at home’on the educational outcomes of vulnerable children in Australia during the COVID-19 pandemic. Literature Review Prepared by the National Centre for Student Equity in Higher Education. Curtin University, Australia.

Elaish, M., Shuib, L., Ghani, N., & Yadegaridehkordi, E. (2019). Mobile English language learning (MELL): A literature review. Educational Review, 71 (2), 257–276.

Fawaz, M., Al Nakhal, M., & Itani, M. (2021). COVID-19 quarantine stressors and management among Lebanese students: A qualitative study.  Current Psychology , 1–8.

Franchi, T. (2020). The impact of the Covid-19 pandemic on current anatomy education and future careers: A student’s perspective. Anatomical Sciences Education, 13 (3), 312–315.

Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123 , 150–163.

Gonzalez, T., De La Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M. (2020). Influence of COVID-19 confinement on students’ performance in higher education. PLoS One, 15 (10), e0239490.

Hew, K. F., Jia, C., Gonda, D. E., & Bai, S. (2020). Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17 (1), 1–22.

Huang, Q. (2019). Comparing teacher’s roles of F2F learning and online learning in a blended English course. Computer Assisted Language Learning, 32 (3), 190–209.

John Hopkins University. (2021). Global map . https://coronavirus.jhu.edu/

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India . Children and Youth Services Review, 116 , 105194.

Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems, 46 (1), 4–29.

Khalil, R., Mansour, A. E., Fadda, W. A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A., & Al-Wutayd, O. (2020). The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education, 20 (1), 1–10.

Matsumoto, K. (1994). Introspection, verbal reports and second language learning strategy research. Canadian Modern Language Review, 50 (2), 363–386.

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59 (3), 1065–1078.

Pham, T., & Nguyen, H. (2020). COVID-19: Challenges and opportunities for Vietnamese higher education. Higher Education in Southeast Asia and beyond, 8 , 22–24.

Google Scholar  

Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144 , 103701.

Recker, M. M., Dorward, J., & Nelson, L. M. (2004). Discovery and use of online learning resources: Case study findings. Educational Technology & Society, 7 (2), 93–104.

Roblek, V., Mesko, M., Dimovski, V., & Peterlin, J. (2019). Smart technologies as social innovation and complex social issues of the Z generation. Kybernetes, 48 (1), 91–107.

Seplaki, C. L., Agree, E. M., Weiss, C. O., Szanton, S. L., Bandeen-Roche, K., & Fried, L. P. (2014). Assistive devices in context: Cross-sectional association between challenges in the home environment and use of assistive devices for mobility. The Gerontologist, 54 (4), 651–660.

Simbulan, N. (2020). COVID-19 and its impact on higher education in the Philippines. Higher Education in Southeast Asia and beyond, 8 , 15–18.

Singh, K., Srivastav, S., Bhardwaj, A., Dixit, A., & Misra, S. (2020). Medical education during the COVID-19 pandemic: a single institution experience. Indian Pediatrics, 57 (7), 678–679.

Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education, 33 (4), 289–306.

Spector, P. (1994). Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of Organizational Behavior, 15 (5), 385–392.

Suryaman, M., Cahyono, Y., Muliansyah, D., Bustani, O., Suryani, P., Fahlevi, M., & Munthe, A. P. (2020). COVID-19 pandemic and home online learning system: Does it affect the quality of pharmacy school learning? Systematic Reviews in Pharmacy, 11 , 524–530.

Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76 (1), 93–135.

Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19.  Interactive Learning Environments , 1–12.

Usher, M., & Barak, M. (2020). Team diversity as a predictor of innovation in team projects of face-to-face and online learners. Computers & Education, 144 , 103702.

Varea, V., & González-Calvo, G. (2020). Touchless classes and absent bodies: Teaching physical education in times of Covid-19.  Sport, Education and Society , 1–15.

Wallace, R. M. (2003). Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information, 3 (2), 241–280.

World Health Organization (2020). Coronavirus . https://www.who.int/health-topics/coronavirus#tab=tab_1

Xue, E., Li, J., Li, T., & Shang, W. (2020). China’s education response to COVID-19: A perspective of policy analysis.  Educational Philosophy and Theory , 1–13.

Download references

No funding was received in the conduct of this study.

Author information

Authors and affiliations.

College of Education, Arts and Sciences, National University, Manila, Philippines

Jessie S. Barrot, Ian I. Llenares & Leo S. del Rosario

You can also search for this author in PubMed   Google Scholar

Contributions

Jessie Barrot led the planning, prepared the instrument, wrote the report, and processed and analyzed data. Ian Llenares participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing. Leo del Rosario participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing.

Corresponding author

Correspondence to Jessie S. Barrot .

Ethics declarations

Ethics approval.

The study has undergone appropriate ethics protocol.

Consent to participate

Informed consent was sought from the participants.

Consent for publication

Authors consented the publication. Participants consented to publication as long as confidentiality is observed.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

Download citation

Received : 22 January 2021

Accepted : 17 May 2021

Published : 28 May 2021

Issue Date : November 2021

DOI : https://doi.org/10.1007/s10639-021-10589-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Remote learning
• Online learning
• Online learning strategies
• Higher education
• Find a journal
• Publish with us
• Track your research

Capturing the benefits of remote learning

How education experts are applying lessons learned in the pandemic to promote positive outcomes for all students

Vol. 52 No. 6 Print version: page 46

• Schools and Classrooms

With schools open again after more than a year of teaching students outside the classroom, the pandemic sometimes feels like a distant memory. The return to classrooms this fall brings major relief for many families and educators. Factors such as a lack of reliable technology and family support, along with an absence of school resources, resulted in significant academic setbacks, not to mention stress for everyone involved.

But for all the downsides of distance learning, educators, psychologists, and parents have seen some benefits as well. For example, certain populations of students found new ways to be more engaged in learning, without the distractions and difficulties they faced in the classroom, and the general challenges of remote learning and the pandemic brought mental health to the forefront of the classroom experience.

Peter Faustino, PsyD, a school psychologist in Scarsdale, New York, said the pandemic also prompted educators and school psychologists to find creative new ways of ensuring students’ emotional and academic well-being. “So many students were impacted by the pandemic, so we couldn’t just assume they would find resources on their own,” said Faustino. “We had to work hard at figuring out new ways to connect with them.”

Here are some of the benefits of distance learning that school psychologists and educators have observed and the ways in which they’re implementing those lessons post-pandemic, with the goal of creating a more equitable, productive environment for all students.

Prioritizing mental health

Faustino said that during the pandemic, he had more mental health conversations with students, families, and teachers than ever. “Because COVID-19 affected everyone, we’re now having mental health discussions as school leaders on a daily and weekly basis,” he said.

This renewed focus on mental health has the potential to improve students’ well-being in profound ways—starting with helping them recover from the pandemic’s effects. In New York City, for example, schools are hiring more than 600 new clinicians, including psychologists , to screen students’ mental health and help them process pandemic-related trauma and adjust to the “new normal” of attending school in person.

Educators and families are also realizing the importance of protecting students’ mental health more generally—not only for their health and safety but for their learning. “We’ve been seeing a broader appreciation for the fact that mental health is a prerequisite for learning rather than an extracurricular pursuit,” said Eric Rossen, PhD, director of professional development and standards at the National Association of School Psychologists.

As a result, Rossen hopes educators will embed social and emotional learning components into daily instruction. For example, teachers could teach mindfulness techniques in the classroom and take in-the-moment opportunities to help kids resolve conflicts or manage stress.

Improved access to mental health resources in schools is another positive effect. Because of physical distancing guidelines, school leaders had to find ways to deliver mental health services remotely, including via online referrals and teletherapy with school psychologists and counselors.

Early in the pandemic, Faustino said he was hesitant about teletherapy’s effectiveness; now, he hopes to continue offering a virtual option. Online scheduling and remote appointments make it easier for students to access mental health resources, and some students even enjoy virtual appointments more, as they can attend therapy in their own spaces rather than showing up in the counselor’s office. For older students, Faustino said that level of comfort often leads to more productive, open conversations.

Autonomy as a key to motivation

Research suggests that when students have more choices about their materials and activities, they’re more motivated—which may translate to increased learning and academic success. In a 2016 paper, psychology researcher Allan Wigfield, PhD, and colleagues make the case that control and autonomy in reading activities can improve both motivation and comprehension ( Child Development Perspectives , Vol. 10, No. 3 ).

During the period of online teaching, some students had opportunities to learn at their own pace, which educators say improved their learning outcomes—especially in older students. In a 2020 survey of more than 600 parents, researchers found the second-most-valued benefit of distance learning was flexibility—not only in schedule but in method of learning.

In a recent study, researchers found that 18% of parents pointed to greater flexibility in a child’s schedule or way of learning as the biggest benefit or positive outcome related to remote learning ( School Psychology , Roy, A., et al., in press).

This individualized learning helps students find more free time for interests and also allows them to conduct their learning at a time they’re most likely to succeed. During the pandemic, Mark Gardner, an English teacher at Hayes Freedom High School in Camas, Washington, said he realized how important student-centered learning is and that whether learning happens should take precedence over how and when it occurs.

For example, one of his students thrived when he had the choice to do work later at night because he took care of his siblings during the day. Now, Gardner posts homework online on Sundays so students can work at their own pace during the week. “Going forward, we want to create as many access points as we can for kids to engage with learning,” he said.

Rosanna Breaux , PhD, an assistant professor of psychology and assistant director of the Child Study Center at Virginia Tech, agrees. “I’d like to see this flexibility continue in some way, where—similar to college—students can guide their own learning based on their interests or when they’re most productive,” she said.

During the pandemic, many educators were forced to rethink how to keep students engaged. Rossen said because many school districts shared virtual curricula during the period of remote learning, older students could take more challenging or interesting courses than they could in person. The same is true for younger students: Megan Hibbard, a teacher in White Bear Lake, Minnesota, said many of her fifth graders enjoyed distance learning more than in-person because they could work on projects that aligned with their interests.

“So much of motivation is discovering the unique things the student finds interesting,” said Hunter Gehlbach, PhD, a professor and vice dean at the Johns Hopkins School of Education. “The more you can facilitate students spending more time on the things they’re really interested in, the better.”

Going forward, Rossen hopes virtual curricula will allow students greater opportunities to pursue their interests, such as by taking AP classes, foreign languages, or vocational electives not available at their own schools.

Conversely, Hibbard’s goal is to increase opportunities for students to pursue their interests in the in-person setting. For example, she plans to increase what she calls “Genius Hours,” a time at the end of the school day when students can focus on high-interest projects they’ll eventually share with the class.

Better understanding of children's needs

One of the most important predictors of a child’s success in school is parental involvement in their education. For example, in a meta-analysis of studies, researchers linked parental engagement in their middle schoolers’ education with greater measures of success (Hill, N. E., & Tyson, D. F., Developmental Psychology , Vol. 45, No. 3, 2009).

During the pandemic, parents had new opportunities to learn about their kids and, as a result, help them learn. According to a study by Breaux and colleagues, many parents reported that the pandemic allowed them a better understanding of their child’s learning style, needs, or curriculum.

James C. Kaufman , PhD, a professor of educational psychology at the University of Connecticut and the father of an elementary schooler and a high schooler, said he’s had a front-row seat for his sons’ learning for the first time. “Watching my kids learn and engage with classmates has given me some insight in how to parent them,” he said.

Stephen Becker , PhD, a pediatric psychologist at Cincinnati Children’s Hospital Medical Center, said some parents have observed their children’s behavior or learning needs for the first time, which could prompt them to consider assessment and Individualized Education Program (IEP) services. Across the board, Gehlbach said parents are realizing how they can better partner with schools to ensure their kids’ well-being and academic success.

For example, Samantha Marks , PsyD, a Florida-based clinical psychologist, said she realized how much help her middle school daughter, a gifted and talented student with a 504 plan (a plan for how the school will offer support for a student’s disability) for anxiety, needed with independence. “Bringing the learning home made it crystal clear what we needed to teach our daughter to be independent and improve executive functioning” she said. “My takeaway from this is that more parents need to be involved in their children’s education in a healthy, helpful way.”

Marks also gained a deeper understanding of her daughter’s mental health needs. Through her 504 plan, she received help managing her anxiety at school—at home, though, Marks wasn’t always available to help, which taught her the importance of helping her daughter manage her anxiety independently.

Along with parents gaining a deeper understanding of their kids’ needs, the pandemic also prompted greater parent participation in school. For example, Rossen said his kids’ school had virtual school board meetings; he hopes virtual options continue for events like back-to-school information sessions and parenting workshops. “These meetings are often in the evening, and if you’re a single parent or sole caregiver, you may not want to pay a babysitter in order to attend,” he said.

Brittany Greiert, PhD, a school psychologist in Aurora, Colorado, says culturally and linguistically diverse families at her schools benefited from streamlined opportunities to communicate with administrators and teachers. Her district used an app that translates parent communication into 150 languages. Parents can also remotely participate in meetings with school psychologists or teachers, which Greiert says she plans to continue post-pandemic.

Decreased bullying

During stay-at-home orders, kids with neurodevelopmental disorders experienced less bullying than pre-pandemic (McFayden, T. C., et al., Journal of Rural Mental Health , No. 45, Vol. 2, 2021). According to 2019 research, children with emotional, behavioral, and physical health needs experience increased rates of bullying victimization ( Lebrun-Harris, L. A., et al., ), and from the U.S. Department of Education suggests the majority of bullying takes place in person and in unsupervised areas (PDF) .

Scott Graves , PhD, an associate professor of educational studies at The Ohio State University and a member of APA’s Coalition for Psychology in Schools and Education (CPSE), said the supervision by parents and teachers in remote learning likely played a part in reducing bullying. As a result, he’s less worried his Black sons will be victims of microaggressions and racist behavior during online learning.

Some Asian American families also report that remote learning offered protection against racism students may have experienced in person. Shereen Naser, PhD, an associate professor of psychology at Cleveland State University and a member of CPSE, and colleagues found that students are more comfortable saying discriminatory things in school when their teachers are also doing so; Naser suspects this trickle-down effect is less likely to happen when students learn from home ( School Psychology International , 2019).

Reductions in bullying and microaggressions aren’t just beneficial for students’ long-term mental health. Breaux said less bullying at school results in less stress, which can improve students’ self-esteem and mood—both of which impact their ability to learn.

Patricia Perez, PhD, an associate professor of international psychology at The Chicago School of Professional Psychology and a member of CPSE, said it’s important for schools to be proactive in providing spaces for support and cultural expression for students from vulnerable backgrounds, whether in culture-specific clubs, all-school assemblies that address racism and other diversity-related topics, or safe spaces to process feelings with teachers.

According to Rossen, many schools are already considering how to continue supporting students at risk for bullying, including by restructuring the school environment.

One principal, Rossen said, recently switched to single-use bathrooms to avoid congregating in those spaces once in-person learning commences to maintain social distancing requirements. “The principal received feedback from students about how going to the bathroom is much less stressful for these students in part due to less bullying,” he said.

More opportunities for special needs students

In Becker and Breaux’s research, parents of students with attention-deficit/hyperactivity disorder (ADHD), particularly those with a 504 plan and IEP, reported greater difficulties with remote learning. But some students with special learning needs—including those with IEPs and 504 plans—thrived in an at-home learning environment. Recent reporting in The New York Times suggests this is one reason many students want to continue online learning.

According to Cara Laitusis, PhD, a principal research scientist at Educational Testing Service ( ETS ) and a member of CPSE, reduced distractions may improve learning outcomes for some students with disabilities that impact attention in a group setting. “In assessments, small group or individual settings are frequently requested accommodations for some students with ADHD, anxiety, or autism. Being in a quiet place alone without peers for part of the instructional day may also allow for more focus,” she said. However, she also pointed out the benefits of inclusion in the classroom for developing social skills with peers.

Remote learning has improved academic outcomes for students with different learning needs, too. Marks said her seventh-grade daughter, a visual learner, appreciated the increase in video presentations and graphics. Similarly, Hibbard said many of her students who struggle to grasp lessons on the first try have benefited from the ability to watch videos over again until they understand. Post-pandemic, she plans to record bite-size lessons—for example, a 1-minute video of a long division problem—so her students can rewatch and process at their own rate.

Learners with anxiety also appreciate the option not to be in the classroom, because the social pressures of being surrounded by peers can make it hard to focus on academics. “Several of my students have learned more in the last year simply due to the absence of anxiety,” said Rosie Reid, an English teacher at Ygnacio Valley High School in Concord, California, and a 2019 California Teacher of the Year. “It’s just one less thing to negotiate in a learning environment.”

On online learning platforms, it’s easier for kids with social anxiety or shyness to participate. One of Gardner’s students with social anxiety participated far more in virtual settings and chats. Now, Gardner is brainstorming ways to encourage students to chat in person, such as by projecting a chat screen on the blackboard.

Technology has helped school psychologists better engage students, too. For example, Greiert said the virtual setting gave her a new understanding of her students’ personalities and needs. “Typing out their thoughts, they were able to demonstrate humor or complex thoughts they never demonstrated in person,” she said. “I really want to keep incorporating technology into sessions so kids can keep building on their strengths.”

Reid says that along with the high school students she teaches, she’s seen her 6-year-old daughter benefit from learning at her own pace in the familiarity of her home. Before the pandemic, she was behind academically, but by guiding her own learning—writing poems, reading books, playing outside with her siblings—she’s blossomed. “For me, as both a mother and as a teacher, this whole phenomenon has opened the door to what education can be,” Reid said.

Eleanor Di Marino-Linnen, PhD, a psychologist and superintendent of the Rose Tree Media School District in Media, Pennsylvania, says the pandemic afforded her district a chance to rethink old routines and implement new ones. “As challenging as it is, it’s definitely an exciting time to be in education when we have a chance to reenvision what schools have looked like for many years,” she said. “We want to capitalize on what we’ve learned.”

Further reading

Why are some kids thriving during remote learning? Fleming, N., Edutopia, 2020

Remote learning has been a disaster for many students. But some kids have thrived. Gilman, A., The Washington Post , Oct. 3, 2020

A preliminary examination of key strategies, challenges, and benefits of remote learning expressed by parents during the COVID-19 pandemic Roy, A., et al., School Psychology , in press

Remote learning during COVID-19: Examining school practices, service continuation, and difficulties for adolescents with and without attention-deficit/hyperactivity disorder Becker S. P., et al., Journal of Adolescent Health , 2020

Recommended Reading

Contact APA

You may also like.

• Reference Manager
• Simple TEXT file

People also looked at

Original research article, engagement in online learning: student attitudes and behavior during covid-19.

• 1 Department of Mathematics, University of California, San Diego, San Diego, CA, United States
• 2 Halıcıo ǧ lu Data Science Institute, University of California, San Diego, San Diego, CA, United States
• 3 Joint Doctoral Program in Math and Science Education, San Diego State University, San Diego, CA, United States
• 4 Joint Doctoral Program in Math and Science Education, University of California, San Diego, San Diego, CA, United States
• 5 Department of Physical Therapy, Movement and Rehabilitation Science, Bouvé College of Health, Northeastern University, Boston, MA, United States
• 6 Art and Design, College of Arts, Media and Design, Northeastern University, Boston, MA, United States
• 7 Qualcomm Institute, University of California, San Diego, San Diego, CA, United States

The COVID-19 pandemic resulted in nearly all universities switching courses to online formats. We surveyed the online learning experience of undergraduate students ( n = 187) at a large, public research institution in course structure, interpersonal interaction, and academic resources. Data was also collected from course evaluations. Students reported decreases in live lecture engagement and attendance, with 72 percent reporting that low engagement during lectures hurt their online learning experience. A majority of students reported that they struggled with staying connected to their peers and instructors and managing the pace of coursework. Students had positive impressions, however, of their instructional staff. Majorities of students felt more comfortable asking and answering questions in online classes, suggesting that there might be features of learning online to which students are receptive, and which may also benefit in-person classes.

Introduction

In Spring 2020, 90% of higher education institutions in the United States canceled in-person instruction and shifted to emergency remote teaching (ERT) due to the COVID-19 pandemic ( Lederman, 2020 ). ERT in response to COVID-19 is qualitatively different from typical online learning instruction as students did not self-select to participate in ERT and teachers were expected to transition to online learning in an unrealistic time frame ( Brooks et al., 2020 ; Hodges et al., 2020 ; Johnson et al., 2020 ). This abrupt transition left both faculty and students without proper preparation for continuing higher education in an online environment.

In a random sample of 1,008 undergraduates who began their Spring 2020 courses in-person and ended them online, 51% of respondents said they were very satisfied with their course before the pandemic, and only 19% were very satisfied after the transition to online learning ( Means and Neisler, 2020 ). Additionally, 57% of respondents said that maintaining interest in the course material was “worse online,” 65% claimed they had fewer opportunities to collaborate with peers, and 42% said that keeping motivated was a problem ( Means and Neisler, 2020 ). Another survey of 3,089 North American higher education students had similar results with 78% of respondents saying online experiences were not engaging and 75% saying they missed face-to-face interactions with instructors and peers ( Read, 2020 ). Lastly, of the 97 university presidents surveyed in the United States by Inside Higher Ed , 81% claimed that maintaining student engagement would be challenging when moving classes online due to COVID-19 ( Inside Higher Ed, 2020 ).

In this report, we consider the measures and strategies that were implemented to engage students in online lectures at UCSD during ERT due to the COVID-19 pandemic. We investigate student perceptions of these measures and place our findings in the larger context of returning to in-person instruction and improving engagement in both online and in-person learning for undergraduates. Before diving into the current study, we first define what we mean by engagement.

Theoretical Framework and Literature Review

Student engagement.

Student engagement has three widely accepted dimensions: behavioral, cognitive and affective ( Chapman, 2002 ; Fredricks et al., 2004 , 2016 ; Mandernach, 2015 ). Each dimension has indicators ( Fredricks et al., 2004 ), or facets ( Coates, 2007 ), that manifest each dimension. Behavioral engagement refers to active responses to learning activities and is indicated by participation, persistence, and/or positive conduct. Cognitive engagement includes mental effort in learning activities and is indicated by deep learning, self-regulation, and understanding. Affective engagement is the emotional investment in learning activities and is indicated by positive reactions to the learning environment, peers, and teachers as well as a sense of belonging. A list of indicators for each dimension can be found in Bond et al. (2020) .

The literature also theorizes different influences for each engagement dimension. Most influencing factors are sociocultural in nature and can include the political, social, and teaching environment as well as relationships within the classroom ( Kahu, 2013 ). In particular, social engagement with peers and instructors creates a sense of community, which is often correlated with more effective learning outcomes ( Rovai and Wighting, 2005 ; Liu et al., 2007 ; Lear et al., 2010 ; Kendricks, 2011 ; Redmond et al., 2018 ; Chatterjee and Correia, 2020 ). Three key classroom interactions are often investigated when trying to understand the factors influencing student engagement: student-student interactions, student-instructor interactions, and student-content interactions ( Moore, 1993 ).

Student-student interactions prevent boredom and isolation by creating a dynamic sense of community ( Martin and Bolliger, 2018 ). Features that foster student-student interactions in online learning environments include group activities, peer assessment, and use of virtual communication spaces such as social media, chat forums, and discussion boards ( Revere and Kovach, 2011 ; Tess, 2013 ; Banna et al., 2015 ). In the absence of face-to-face communication, these virtual communication spaces help build student relationships ( Nicholson, 2002 ; Harrell, 2008 ). In a survey of 1,406 university students in asynchronous online courses, the students claimed to have greater satisfaction and to have learned more when more of the course grade was based on discussions, likely because discussions fostered increased student-student and student-instructor interactions ( Shea et al., 2001 ). Interestingly, in another study, graduate students in online courses claimed that student-student interactions were the least important of the three for maintaining student engagement, but that they were more likely to be engaged if an online course had online communication tools, ice breakers, and group activities ( Martin and Bolliger, 2018 ).

In the Martin and Bolliger (2018) study, the graduate students enrolled in online courses found student-instructor interactions to be the most important of the three interaction types, which supports prior work that found students perceive student-instructor interactions as more important than peer interactions in fostering engagement ( Swan and Shih, 2005 ). Student-instructor interactions increased in frequency in online classes when the following practices were implemented (1) multiple open communication channels between students and instructors ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Martin and Bolliger, 2018 ), (2) regular communication of announcements, reminders, grading rubrics, and expectations by instructors ( Martin and Bolliger, 2018 ), (3) timely and consistent feedback provided to students ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Chakraborty and Nafukho, 2014 ; Martin and Bolliger, 2018 ), and (4) instructors taking a minimal role in course discussions ( Mandernach et al., 2006 ; Dixson, 2010 ).

Student-content interactions include any interaction the student has with course content. Qualities that have been shown to increase student engagement with course content include the use of curricular materials and classroom activities that incorporate realistic scenarios, prompts that scaffold deep reflection and understanding, multimedia instructional materials, and those that allow student agency in choice of content or activity format ( Abrami et al., 2012 ; Wimpenny and Savin-Baden, 2013 ; Britt et al., 2015 ; Martin and Bolliger, 2018 ). In online learning, students need to be able to use various technologies in order to be able to engage in student-content interactions, so technical barriers such as lack of access to devices or reliable internet can be a substantial issue that deprives educational opportunities especially for students from lower socioeconomic households ( Means and Neisler, 2020 ; Reich et al., 2020 ; UNESCO, 2020 ).

Engagement in Online Learning

Bond and Bedenlier (2019) present a theoretical framework for engagement in online learning that combines the three dimensions of engagement, types of interactions that can influence the engagement dimensions, and possible short term and long term outcomes. The types of interactions are based on components present in the student’s immediate surrounding or microsystem, and are largely based on Moore’s three types of interactions: teachers, peers, and curriculum. However, the authors add technology and the classroom environment as influential components because they are particularly important for online learning.

Specific characteristics of each microsystem component can differentially modulate student engagement, and each component has at least one characteristic that specifically focuses on technology. Teacher presence, feedback, support, time invested, content expertise, information and communications technology skills and knowledge, technology acceptance, and use of technology all can influence the types of interactions students might have with their teachers which would then impact their engagement ( Zhu, 2006 ; Beer et al., 2010 ; Zepke and Leach, 2010 ; Ma et al., 2015 ; Quin, 2017 ). For curriculum/activities, the quality, design, difficulty, relevance, level of required collaboration, and use of technology can influence the types of interactions a student might encounter that could impact their engagement ( Zhu, 2006 ; Coates, 2007 ; Zepke and Leach, 2010 ; Bundick et al., 2014 ; Almarghani and Mijatovic, 2017 ; Xiao, 2017 ). Characteristics that can change the quantity and quality of peer interactions and thereby influence engagement include the amount of opportunities to collaborate, formation of respectful relationships, clear boundaries and expectations, being able to physically see each other, and sharing work with others and in turn respond to the work of others ( Nelson Laird and Kuh, 2005 ; Zhu, 2006 ; Yildiz, 2009 ; Zepke and Leach, 2010 ). When describing influential characteristics, the authors combine classroom environment and technology because in online learning, the classroom environment inherently utilizes technology. The influential characteristics of these two components are access to technology, support in using and understanding technology, usability, design, technology choice, sense of community, and types of assessment measures. All of these characteristics demonstrably influenced engagement levels in prior literature ( Zhu, 2006 ; Dixson, 2010 ; Cakir, 2013 ; Levin et al., 2013 ; Martin and Bolliger, 2018 ; Northey et al., 2018 ; Sumuer, 2018 ).

Online learning can take place in different formats, including fully synchronous, fully asynchronous, or blended ( Fadde and Vu, 2014 ). Each of these formats offers different challenges and opportunities for technological ease, time management, community, and pacing. Fully asynchronous learning is time efficient, but offers less opportunity for interactions that naturally take place in person ( Fadde and Vu, 2014 ). Instructors and students may feel underwhelmed by the lack of immediate feedback that can happen in face to face class time ( Fadde and Vu, 2014 ). Synchronous online learning is less flexible for teachers and students and requires reliable technology, but allows for more real time engagement and feedback ( Fadde and Vu, 2014 ). In blended learning courses, instructors have to coordinate and organize both the online and in person meetings and lessons, which is not as time efficient. Blended learning means there is some in person engagement which provides spontaneity and more natural personal relations ( Fadde and Vu, 2014 ). In all online formats, students may feel isolated and instructors and students need to spend more time and intention into building community ( Fadde and Vu, 2014 ; Gillett-Swan, 2017 ). Often, instructors can use learning management systems and discussion boards to help facilitate student interaction and connection ( Fadde and Vu, 2014 ). In terms of group work, engagement and participation is dependent not only on the modality of learning, but also the instructors expectations for assessment ( Gillett-Swan, 2017 ). Given the flexibility and power of online meeting and work environments, collaborating synchronously or asynchronously are both possible and effective ( Gillett-Swan, 2017 ). In online learning courses, especially fully asynchronous, students are more accountable for their learning, which may be challenging for students who struggle with self-regulating their work pace ( Gillett-Swan, 2017 ). Learning from home also means there are more distractions than when students attend class on campus. At any point during class, children, pets, or work can interrupt a student’s, or instructor’s, remote learning or teaching ( Fadde and Vu, 2014 ).

According to Raes et al. (2019) , the flexibility of a blended -or hybrid- learning environment encourages more students to show up to class when they otherwise would have taken a sick day, or would not have been able to attend due to home demands. It also equalizes learning opportunities for underrepresented groups, and more comprehensive support with two modes of interaction. On the other hand, hybrid learning can cause more strain on the instructor who may have to adapt their teaching designs for the demands of this unique format while maintaining the same standards ( Bülow, 2022 ). Due to the nature of class, some students can feel more distant to the instructor and to each other, and in many cases active class participation was difficult in hybrid learning environments. Although Bulow’s review (2022) focused on the challenges and opportunities of designing effective hybrid learning environments for the teacher, it follows that students participating in different environments will also need to adapt to foster effective active participation environments that encompass both local and remote learners.

Engagement in Emergency Remote Instruction During COVID-19

There is currently a thin literature on student perceptions of the efficacy of ERT strategies and formats in engaging students during COVID-19. Indeed, student perceptions about online learning do not indicate actual learning. This study considers student perceptions for the purpose of gathering information about what conditions help or hinder students’ comfort with engaging in online classes toward the goal of designing improved online learning opportunities in the future. The large scale surveys of undergraduate students had some items relating to engagement, but these surveys aimed to generally understand the student experience during the transition to COVID-19 induced ERT ( Means and Neisler, 2020 ; Read, 2020 ). A few small studies have surveyed or interviewed students from a single course on their perceptions of the changes made to courses to accommodate ERT ( Senn and Wessner, 2021 ), the positives and negatives of ERT ( Hussein et al., 2020 ), or the changes in their participation patterns and the course structures and instructor strategies that increase or decrease engagement in ERT ( Perets et al., 2020 ). In their survey of 73 students across the United States, Wester and colleagues specifically focused on changes to students’ cognitive, affective, and behavioral engagement due to COVID-19 induced ERT, but they did not inquire as to what were the key influencing factors for these changes. Walker and Koralesky (2021) and Shin and Hickey (2021) surveyed students from a single institution but from multiple courses and thus are most relevant to the current study. These studies aimed to understand the students’ perceptions of their engagement and influencing factors of engagement at a single institution, but they did not assess how often these factors were implemented at that institution.

The current study investigates the engagement strategies used in a large, public, research institution, students’ opinions about these course methods, and students’ overall perception of learning in-person versus during ERT. This study aims to answer the following questions:

1. How has the change from in-person to online learning affected student attendance, performance expectations of students, and participation in lectures?

2. What engagement tools are being utilized in lectures and what do students think about them?

3. What influence do social interactions with peers, teachers, and administration have on student engagement?

These three questions encompass the three different dimensions of engagement, including multiple facets of each, as well as explicitly highlighting the role of technology in student engagement.

Materials and Methods

Data were collected from two main sources: a survey of undergraduates, and Course and Professor Evaluations (CAPE). The study was deemed exempt from further review by the institution’s Institutional Review Board because identifying information was not collected.

The survey consisted of 50 questions, including demographic information as well as questions about both in-person and online learning (Refer to full survey in Supplementary Material.). The survey, hosted on Qualtrics, was distributed to undergraduate students using various social media channels, such as Reddit, Discord, and Facebook, in addition to being advertised in some courses. In total, the survey was answered by 237 students, of which 187 completed the survey in full, between January 26th and February 15, 2021. It was made clear to students that the data collected would be anonymous and used to assess engagement over the course of Fall 2019 to Fall 2020. The majority of the survey was administered using five-point Likert scales of agreement, frequency, and approval. The survey was divided into blocks, each of which used the same Likert scale. Quantitative analysis of the survey data was conducted using R, and visualized with the likert R package ( Bryer and Speerschneider, 2016 ).

A number of steps were taken to ensure that survey responses were valid. Before survey distribution, 2 cognitive interviews were conducted with undergraduate students attending the institution in order to refine the intelligibility of survey items ( Desmione and Carlson Le Floch, 2004 ). Forty-eight incomplete surveys were excluded. In addition, engagement tests were placed within the larger blocks of the survey in order to prevent respondents from clicking the same choice repeatedly without reading the prompts. The two students who answered at least one of these questions incorrectly were excluded.

Respondent Profile

Respondents were asked before the survey to confirm that they were undergraduate students attending the institution over the age of 18. Among the 187 students that filled out the survey in its entirety, 21.9% were in their first year, 28.3% in their second year, 34.2% in their third year, 11.8% in their fourth year, and 1.1% in their fifth year or beyond. It should be noted, therefore, that some students, especially first-years, had no experience with in-person college education at the institution, and these respondents were asked to indicate this for any questions about in-person learning. However, all students surveyed were asked before participating whether they had experience with online learning at the institution. 2.7% of respondents were first year transfers. 72.7% of overall respondents identified as female, 25.7% as male, 0.5% as non-binary, and 1.1% preferred not to disclose gender. In regards to ethnicity, 45.6% of respondents identified as Asian, 22.8% as White, 13.9% as Hispanic/Latinx, 1.7% as Middle Eastern, 1.6% as Black or African-American, and 2.2% as Other. 27.9% of respondents were first-generation college students, 7.7% of respondents were international students, and 9.9% of students were transfer students.

In the most recent report for the 2020–2021 academic year, the Institutional Research Department noted that out of 31,842 undergraduates, 49.8% of undergraduates are women and 49.4% are men ( University of California, San Diego Institutional Research, 2021 ). This report states that 17% of undergraduates are international students, which is a larger percentage than is represented by survey respondents ( University of California, San Diego Institutional Research, 2021 ). The institution reports 33% of undergraduates are transfer students, which are also underrepresented in the survey respondents ( University of California San Diego [UCSD], 2021b ). The ethnicity profile of the survey respondents is similar to the undergraduate student demographic at this institution. According to the institutional research report, among undergraduates, 37.1% are Asian American, 19% are White, 20.8% are Chicano/Latino, 3% are African American, 0.4% are American Indian, and 2.5% are missing data on ethnicity ( University of California, San Diego Institutional Research, 2021 ).

Course and Professor Evaluation Reviews

Data were also collected from the institution’s CAPE reviews, a university-administered survey offered prior to finals week every quarter, in which undergraduate students are asked to rate various aspects of their experience with their undergraduate courses and professors ( Courses not CAPEd for Winter 22, 2022 ). CAPE reviews are anonymous, but are sometimes incentivized by professors to increase participation.

Although it was not designed with Bond and Bedenlier’s student engagement framework in mind, the questions on the CAPE survey still address the fundamental influences on engagement established by the framework. The CAPE survey asks students how many hours a week they spend studying outside of class, the grade they expect to receive, and whether they recommend the course overall. The survey then asks questions about the professor, such as whether they explain material well, show concern for student learning, and whether the student recommends the professor overall.

In this study, we chose to look only at data from Fall 2019, a quarter where education was in-person, and Fall 2020, when courses were online. In Fall 2019, there were 65,985 total CAPE reviews submitted, out of a total of 114,258 course enrollments in classes where CAPE was made available, for a total response rate of 57.8% ( University of California San Diego [UCSD], 2021a ). The mean response rate within a class was 53.1% with a standard deviation of 20.7%. In Fall 2020, there were 65,845 CAPE responses out of a total of 118,316 possible enrollments, for a total response rate of 55.7%. The mean response rate within a class was 50.7%, with a standard deviation of 19.6%.

In order to adjust for the different course offerings between quarters, and for the different professors who might teach the same course, we selected only CAPE reviews for courses that were offered in both Fall 2019 and Fall 2020 with the same professors. This dataset contained 31,360 unique reviews (16,147 from Fall 2019 and 15,213 from Fall 2020), covering 587 class sections in Fall 2019 and 630 in Fall 2020. Since no data about the students were provided with the set, however, we do not know how many students these 31,360 reviews represent. This pairing strategy offers many interesting opportunities to compare the changes and consistencies of student reviews between both quarters in question. To keep this study focused on the three research questions and in observation of time and space limitations, analysis was only performed on the pairwise level of the general CAPE survey questions and not broken down to further granularity.

The CAPE survey was created by the designers of CAPE, not the researchers of this paper. The questions on the CAPE survey are general and only provide a partial picture of the status of student engagement in Fall 2019 and Fall 2020. The small scale survey created by this research team attempts to clarify and make meaning of the results from the CAPE data.

Data Analysis

Survey data.

Survey data was collected and exported from Qualtrics as a. csv file, then manually trimmed to include only relevant survey responses from participants who completed the survey. Data analysis was done in R using the RStudio interface, with visualizations done using the likert and ggplot2 R packages ( Bryer and Speerschneider, 2016 ; Wickham, 2016 ; R Core Team, 2020 ; RStudio Team, 2020 ). Statistical tests were performed on lecture data, using paired t -tests, and Mann–Whitney U tests of the responses; for example, when comparing attendance of in-person lectures in Fall 2019 and live online lectures on Zoom in Fall 2020.

Course and Professor Evaluation Data

As previously mentioned, analysis of CAPE reviews was restricted to courses that were offered in both Fall 2019 and 2020 with the same professor, with Fall 2019 courses being in-person and Fall 2020 courses being online. This was done since the variation of interest is the change from in-person to online education, and restricting analysis to these courses allowed the pairing of specific courses for statistical tests, as well as the adjustment for any differences in course offerings or professor choices between the two quarters. In order to compare ratings for a specific item, first, negative items were recoded if necessary. The majority of questions were on a 5-point Likert scale, though some, such as expected grade, needed conversion from categorical (A–F scale) to numerical (usually 0–4). Then, the two-sample Mann–Whitney U test was conducted on the numerical survey answers, comparing the results from Fall 2019 to those from Fall 2020. Results were then visualized using the R package ggplot2 ( Wickham, 2016 ), as well as the likert package ( Bryer and Speerschneider, 2016 ).

In this study, we aimed to take a broad look at the state of online learning at UCSD as compared to in-person learning before the COVID-19 pandemic. This assessment was split into three general categories: changes in lecture engagement and student performance, tools that professors and administrators have implemented in the face of online learning, and changes in patterns of students’ interactions with their peers and with instructors. In general, while we found that students’ ratings of their professors and course staff remained positive, there were significant decreases in lecture engagement, attendance, and perceived ability to keep up with coursework, even as expected grades rose. In addition, student-student interactions fell for the vast majority of students, which students felt hurt their learning experience.

Course and Professor Evaluation Results

How has the change from in-person to online learning affected student attendance, performance expectations, and participation in lectures, lecture attendance.

In the CAPE survey, students reported their answers to a series of questions relating to lecture attendance and engagement. Table 1 reports the results of the Mann–Whitney U test for each question, in which the results from Fall 2019 were compared to the results from Fall 2020. Statistically significant differences were found between students’ responses to the question “How often do you attend this course?” (rated on a 1–3 scale of Very Rarely, Some of the Time, and Most of the Time), although students were still most likely to report that they attended the class most of the time. Statistically significant decreases were also found for students’ agreement to the questions “Instructor is well-prepared for classes,” and “Instructor starts and finishes classes on time.” It should be noted that “attendance” was not clarified as “synchronous” or “asynchronous” attendance to survey respondents.

Table 1. Mean and standard deviations of student responses on CAPE evaluation questions relating to lecture attendance and engagement in Fall 2019 and 2020.

Expected Grades

Within the CAPE survey, students are asked, “What grade do you expect in this class?” The given options are A, B, C, D, F, Pass, and No Pass. The proportion of CAPE responses in which students reported taking the course Pass/No Pass stayed relatively constant from Fall 2019 to Fall 2020, going from 6.5% in Fall 2019 to 6.4% in Fall 2020. As can be seen in Figure 1 , participants were more likely to expect A’s in Fall 2020; in Fall 2019, the median expected grade was an A in 56.8% of classes, while in Fall 2020, this figure was 68.0%. We used a Mann–Whitney U test to test our hypothesis that there would be a difference between Fall 2019 and Fall 2020 expected grades because of students’ and instructors’ unfamiliarity with the online modality. When looking solely at classes in which students expected to receive a letter grade, after recoding letter grades to GPA equivalents, a significant difference was found between expected grades in Fall 2019 and 2020, with a mean of 3.443 in FA19 and 3.538 in FA20 ( U = 92286720, p < 0.001).

Figure 1. Distribution of grades expected by students prior to finals week in CAPE surveys in Fall 2019 and Fall 2020.

What Engagement Tools Are Being Utilized by Professors and What do Students Think About Them?

Assignments and learning.

As part of the CAPE survey, respondents were asked to rate their agreement on a 5-point Likert scale to questions about their assignments and learning experience in the class. Results are displayed in Table 2 . Statistically significant increases in student agreement, as indicated by the two-sample Mann–Whitney U test, were reported in the questions “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course.”

Table 2. Student responses on CAPE evaluation statements relating to assignments, course material, and quality of learning.

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

Professor efficacy and accessibility.

As part of the CAPE survey, students also rated their professors in various aspects, as can be seen in Table 3 . The only significant result observed between Fall 2019 and Fall 2020 was a slight increase in student agreement with the statement “Instructor is accessible outside of class.”

Table 3. Student responses on CAPE evaluation statements relating to instructor efficacy and accessibility.

Survey Results

General satisfaction.

Respondents were asked to indicate their agreement on a 5-point Likert scale (Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, and Strongly Agree) to the statement, “In general, I am satisfied with my online learning experience at [institution].” 36% of respondents agreed with the statement, 28% neither agreed nor disagreed, and 36% disagreed.

Perceptions of Academic Performance

Students were asked to rate their agreement on a 5-point Likert scale of agreement to a series of broad questions about their online learning experience, some of which pertained to academic performance. When assessing the statement “My current online courses are more difficult than my past in-person courses,” 42% chose Strongly Agree or Agree, 32% chose Neither Agree nor Disagree, and 26% chose Disagree or Strongly Disagree. Respondents were also split on the statement “My academic performance has improved with online education,” which 28% agreed/strongly agreed with, 34% disagreed/strongly disagreed with, and 38% chose neither.

For the statement “I feel more able to manage my time effectively with online education than with in-person education,” only 34% agreed/strongly agreed with the statement while 45% disagreed/strongly disagreed and 21% chose neither. For the statement, “I feel that it is easier to deal with the pace of my course load with online education than with in-person education,” 30% of respondents agreed/strongly agreed, 54% disagreed/strongly disagreed, and 16% neither agreed nor disagreed.

Lecture Attendance by Class Type

Since the CAPE survey question regarding attendance did not specify asynchronous or synchronous attendance, students were asked on the survey created by the authors of this paper how often they attended and skipped certain types of lectures. In response to the question “During your last quarter of in-person classes, how often did you skip live, in-person lectures?,” 11% reported doing so often or always, 14% did so sometimes, and the remaining 74% did so rarely or never. The terms “Sometimes” and “Rarely” were not clarified to the respondents. This is the same scale and language used on the CAPE survey, however, which was a benefit to synthesizing and comparing this data with CAPEs. Meanwhile, for online classes, 35% reported skipping their live classes often or always, 23% did so sometimes, and 43% did so rarely or never.

Respondents were also asked about their recorded lectures, both in-person and online; while some courses at the institution are recorded and released in either audio or video form for students, most online synchronous lectures are recorded. When asked how often they watched recorded lectures instead of live lectures in-person, 12% of respondents said they did so often or always, 12% reported doing so sometimes, and 76% did so rarely or never. For online classes where recorded versions of live lectures were available, 47% of students reported watching the recorded version often or always, 21% did so sometimes, and 33% did so rarely or never.

Meanwhile, there were also some lectures during online learning that were offered only online (asynchronous), as opposed to being recorded versions of lectures that were delivered to students live over Zoom.

Students were asked questions about their lecture attendance for in-person learning pre-COVID and for online learning during the pandemic. On a 5 point Likert scale from Never to Always, 11% of students said they skipped “live, in-person lectures” in their courses pre-COVID Often or Always. On the same scale, 35% of respondents said they skipped live online lectures Often or Always. To assess the significance of these reports, we conducted a one-sided Mann–Whitney U test with the null hypothesis that the median frequency of students skipping live online lectures is greater than the median frequency of skipping live in-person lectures. Previous research suggesting that lecture attendance decreased after the COVID-19 transition motivated our alternative hypothesis that students would skip live online lectures more often ( Perets et al., 2020 ). The result was significant, meaning that this evidence suggests that students skip online lectures (Mdn = 3 “Sometimes”) more often than live in-person lectures (Mdn = 2 “Rarely”), U = 23328, p < 0.001. The results were also significant when a one-sided 2 sample t -test was performed to test if students were skipping online lectures ( M = 2.84, SD = 1.13) more often than they skipped in-person lectures ( M = 1.97, SD = 1.06), t (358.53) = 7.55, p < 0.001.

In order to clarify why students might be skipping lectures, we asked students how often they were using the recorded lecture options during in-person and online learning. 12% of respondents reported that they watched the recorded lecture “Often” or “Always” instead of attending the live lecture in-person while 47% of respondents said that they watched the recorded version of lecture, if it was offered, “Often” or “Always” rather than the live version during remote learning. When a one-sided Mann–Whitney U test was performed comparing the medians of students that utilized the recorded option during in-person classes (Mdn = 2 “Rarely”) and during online classes (Mdn = 3 “Sometimes”), the results were significant, suggesting that more students watch a recorded lecture version when it is offered during online classes, U = 6410, p < 0.001. The results are also significant with a t -test comparing the means of students that watched the recorded format during in-person classes ( M = 1.95, SD = 1.08) and during online classes ( M = 3.23, SD = 1.23), t (330.84) = –10.13, p < 0.001.

Students were asked how often they used course materials, such as a textbook or instructor provided notes and slideshows, rather than attending a live or recorded lecture to learn the necessary material. 10% of students said that they used course materials “Often” or “Always” during in-person learning while 19% of students said they used course materials “Often” or “Always” during online learning. The results were significant in a one-sided Mann–Whitney U test for the null hypothesis that the medians are equivalent for students using materials during in-person learning (Mdn = 1 “Never”) and during online learning (Mdn = 2 “Rarely”), U = 12644, p < 0.001. In other words, the evidence suggests that students use course materials instead of attending lectures more often when classes are online than when classes are in-person. A one-sided t -test also indicates that students during online learning ( M = 2.30, SD = 1.16) utilize provided materials instead of watching lecture to learn course material more often than students during in-person learning ( M = 1.76, SD = 1.03), t (364.55) = –4.72, p < 0.001.

Discussions are supplementary and sometimes mandatory classes to the lecture conducted by a teaching assistant. Students reported that during the last quarter of online classes the discussion sections tended to include synchronous live discussion instead of pre-recorded content (see Table 4 ).

Table 4. Distribution of survey responses to questions about non-mandatory discussion sections.

Reported Attendance and Engagement in Lecture

Students were asked to rate their agreement on the same 5-point Likert scale to a series of questions about their in-lecture attendance and engagement. When presented with the statement “I feel more comfortable asking questions in online classes than in in-person ones,” 56% of students agreed, 22% neither agreed nor disagreed, and 22% disagreed. Here, “agreed” includes strongly agree and disagree includes “strongly disagreed.” This was similar to the result for “I feel more comfortable answering questions in online classes than in in-person ones,” to which 56% agreed, 24% neither agreed nor disagreed, and 20% disagreed.

When students who had taken both in-person and online courses were directly asked about overall attendance of live lectures, with the statement “I attend more live lectures now that they are online than I did when lectures were in-person,” 12% agreed, 19% neither agreed nor disagreed, and 69% disagreed (with 32.5% selecting “Strongly disagree”).

Issues With Online Learning

Respondents were asked to indicate on a 5-point Likert frequency scale (Never, Rarely, Sometimes, Often, and Always) how often a series of possible issues affected their online learning. These are reported in Figure 2 . The most common technical issue was unreliable WiFi. 20% of students say unreliable WiFi happens “Often” or “Always,” 35% say this issue happens to them “Sometimes,” and 45% of students say unreliable WiFi affects their online learning “Never” or “Rarely.” The next common technological problem students face is unreliable devices. A poor physical environment affected students’ online learning for 32% of the respondents “Often” or “Always.” Issues with platforms, such as Gradescope, Canvas, and Zoom, were present but reported less often.

Figure 2. Prevalence of issues in online education among student survey respondents ( n = 187).

Course Structure

For a given possible intervention in course structure, students were asked how often their professors implemented the changes and to rate their opinion of the learning strategy. The examined changes were weekly quizzes, replacing exams with projects or other assignments, interactive polls or questions during lectures, breakout rooms within lectures, open-book or open-note exams, and optional or no-fault final exams – exams that will not count toward a student’s overall grade if their exam score does not help their grade.

Respondents’ reported frequencies of these interventions are displayed in Figure 3 , and their ratings of them are displayed in Figure 4 . In addition to being the most common intervention, open book exams were also the most popular intervention among students, with 89% of respondents reporting that they had a Good or Excellent opinion. Similarly popular were in-lecture polls, optional finals, and replacing exams with assignments, while breakout sessions had a slightly negative favorability.

Figure 3. Students’ reported frequencies of certain possible interventions in online learning.

Figure 4. Students’ reported approval of certain possible interventions in online learning.

Academic Tools and Resources

In the survey, students were asked to rate their agreement with the statement, “Online learning has made me more likely to use academic resources such as office hours, tutoring, or voluntary discussion sessions.” 42% of students agreed (includes “strongly agreed”), 23% neither agreed nor disagreed, and 35% disagreed (includes “strongly disagreed”). However, for the statement, “Difficulties accessing office hours or other academic resources have negatively interfered with my academic performance during online education,” 26% of students agreed/strongly agreed, 24% neither agreed nor disagreed, and 49% disagreed/strongly disagreed.

Respondents were asked to rate their opinion of various academic resources on a 5-point scale (Terrible, Poor, Average, Good, and Excellent) for both in-person and online classes ( Figures 5 , 6 ). The most notable change in rating was for the messaging platform Discord, which 67% of respondents saw as a Good or Excellent academic resource during online education, compared to 34% in in-person education. The learning management system Canvas also saw an increase in favorability, while favorability decreased for course discussions.

Figure 5. Students’ reported approval ratings of certain academic resources and tools when classes were in-person.

Figure 6. Students’ reported approval ratings of certain academic resources and tools when classes were online.

Respondents were asked to rate the frequency at which they and their professors turned their cameras on during lectures. 64% of students reported keeping their cameras on never or rarely, 29% reported keeping cameras on sometimes, and 6% of students reported keeping their cameras on often or always. Meanwhile, for professors, 58% of students reported that all of their professors kept their cameras on, 28% said most kept their cameras on, 9% said about half did so, and the remaining 5% said that some or none of their professors kept cameras on.

Personal Interaction

A lack of social interaction was among the largest complaints of students about online learning. 88% of respondents at least somewhat agreed with the statement “I feel less socially connected to my peers during online education than with in-person education.” When students were asked how often certain issues negatively impacted their online learning experience, 64% of respondents indicated that a lack of interaction with peers often or always impacted their learning experience, and 44% reported the same about a lack of instructor interaction.

When we asked students how they stay connected to their peers, 78.6% said that they stay connected to peers through student-run course forums, such as Discord, a messaging app that is designed to build communities of a common interest. 72.7% said they use personal communication, i.e., texting, with peers. 48.1% of students said they use faculty-run course forums, such as Piazza or Canvas. 45.5% of students surveyed keep in touch with peers through institution clubs and organizations. 29.4% of students selected that they use student-made study groups and 19.8% stay connected through their campus job.

Ratings of University Faculty and Staff

Students were asked to rate their opinion of various faculty and staff, by answering survey statements of the form “____ have been sufficiently accommodating of my academic needs and circumstances during online learning.” For instructors, 72% agreed/strongly agreed with this statement and 11% disagreed/strongly disagreed; for teaching assistants and course tutors, 81% agreed/strongly agreed, and only 2% disagreed/strongly disagreed. Meanwhile, for university administration, 39% of students agreed/strongly agreed, 34% neither agreed nor disagreed, and 26% disagreed/strongly disagreed.

Based on both the prior literature and this study, students seemed to struggle with engagement before the pandemic during in-person lectures, and it appears from the survey findings that students are struggling even more with engagement in online courses. A U.S. study investigating the teaching and learning experiences of instructors and students during the COVID-19 pandemic also found that when learning transitioned online, students’ main issue was engagement whereas prior to the pandemic the main issue for students was content ( Perets et al., 2020 ). The lack of peer connection and technological issues seem to be significant problems for students during online learning and could contribute to students’ issues with engagement. The problems with attention during an online lecture might be attributed to the lack of social accountability that an in-person lecture promotes to put away distractions like cell phones and taking active notes. Additionally, CAPE data shows that students rate their professors’ efforts and course design highly and similarly before and during Fall 2019 and Fall 2020. Although every course and professor has different requirements, creating collaborative opportunities and incorporating interactive features into lectures could be beneficial to student engagement.

For live lectures, the increase in students reporting skipping live online lectures more often may be due to the increase in availability and ease of recorded options with online lectures. A similar study to this research found that when the university transitioned to Pass/No Pass grading rather than letter grading during ERT, students attended synchronous lectures less ( Perets et al., 2020 ). During the pandemic, the institution’s deadline to change to P/NP grading was extended and more academic departments allowed Pass/No Pass classes to fulfill course requirements. In our study, we did not detect an increase in students who took advantage of the P/NP grading, but it is possible that students skipped more synchronous lectures knowing that they could use the Pass option as a safety net if they did not dedicate the typical amount of lecture time to learn the material. The results emphasize the vital role of the cognitive dimension in engagement.

It is clear that more students are taking advantage of recorded options with online learning. A survey of Harvard medical students indicates a preference for the recorded option because of the ability to increase the speed of the lecture video and prevent fatigue ( Cardall et al., 2008 ). Consistent with previous research, our results suggest that students may seek more value and time management options from course material when classes are fully online ( Perets et al., 2020 ). Recorded lectures allow freedom for students to learn at a time that works best for them ( Rae and McCarthy, 2017 ). For discussions, students reported that they had more discussions that were live rather than recorded. Research indicates that successful online learning requires strong instructor support ( Dixson, 2010 ; Martin and Bolliger, 2018 ). The smaller class setting of a discussion, even virtual, may promote better engagement through interaction among the students, content, and the discussion leader.

Based on CAPE results, which are conducted the week before final exams, students expected higher grades during the online learning period. Although expected grades rose, students concur with previous surveys that the workload was overwhelming and was not adequately adjusted to reflect the circumstances of ERT ( Hussein et al., 2020 ; Shin and Hickey, 2020 ). While there are many factors that could account for this, including the fact that expected grades reported on CAPE do not reflect a student’s actual grade, one possible explanatory factor is the use of more lenient grading standards and course practices during the pandemic. In addition to relaxed Pass/No Pass standards, courses were more likely to adopt practices like open-book tests or no-fault finals, providing students with assessments that emphasized a demonstration of deeper conceptual understanding rather than memorization. It is important to note that students’ perceptions of their learning does not indicate that students are actually learning or performing better academically. This goes for the CAPE question, “I learned a great deal from this course,” the CAPE question about expected grades, and the small scale survey reports about academic performance. We took interest in these questions because they offer insight into the level of difficulty students perceived during ERT due to the shift in engagement demands from remote learning. More research should be done with students’ academic performance data before and after ERT to clarify whether there was a change in students’ learning.

Students’ preference for using a virtual platform during lecture to ask, answer, and respond to questions was surprising. This extends previous evidence from Vu and Fadde (2013) , who found that, in a graduate design course at a Midwestern public university with both in-person and online students in the same lecture, students learning online were more likely to ask questions through a chat than students attending in-person lectures. In addition, during the COVID-19 pandemic, Castelli and Sarvary (2021) report that Zoom chat facilitates discussions for students, especially for those who may not have spoken in in-person classes.

When students were surveyed on the issues they faced with online learning, the most common issues had to do with engagement in lectures, interaction with instructors and peers, and having a poor physical work environment, while technical issues or issues with learning platforms were less common. The distinction between frequency and impact is key, since issues such as bad WiFi connection can be debilitating to online learning even if uncommon, and issues with technology and physical environment also correlate with equity concerns. Other surveys have found that students and faculty from equity-seeking groups faced more hardships during online learning because of increased home responsibilities and problems with internet access ( Chan et al., 2020 ; Shin and Hickey, 2020 ). Promoting student engagement in class involves more than well-planned teaching strategies. Instructors and universities need to look at the resources and accessibility of their class to reduce the digital divide.

According to the CAPE data from Table 2 , instructors received consistent reviews before and after the ERT switch, indicating that they maintained their effectiveness in teaching. The ratings for two CAPE prompts “Instructor is well prepared for class” and “Instructor starts and finishes class on time” had statistically significant decreases from Fall 2019 to Fall 2020. This decrease could be attributed to increased technological preparation needed for online courses and the variety of offerings for lecture modalities. For example, some instructors chose to offer a synchronous lecture at a different time than the original scheduled course time, and then provide office hours during their scheduled lecture time to discuss and review the lectures. Regardless of the statistically significant changes, the means for these two statements are high and similar to Fall 2019.

What Engagement Tools Are Being Utilized in Lectures and What do Students Think About Them?

Based on the results, a majority of students report that their professors are using weekly quizzes, breakout rooms, and polls at least sometimes in their classes to engage students. Students had highly positive ratings of in-course polling, were mostly neutral or positive about weekly quizzes (as a replacement for midterm or final exams), but were slightly negative about breakout rooms. Venton and Pompano (2021) report positive qualitative student feedback from students in chemistry classes at the University of Virginia, with some students finding it easier to speak up and make connections with peers than in an entire class; Fitzgibbons et al. (2021) , meanwhile, found in a sample of 15 students at the University of Rochester that students preferred working as a full class instead of in breakout rooms, though students did report making more peer connections in breakout rooms. Breakouts have potential to strengthen student-student and student-instructor relationships, but further research is needed to clarify their effectiveness.

Changes were also made to course structure, with almost all (94%) of students reporting that open-book exams were used at least sometimes. Open-book exams were also the most popular intervention overall, although the reason for their widespread adoption (academic integrity and fairness concerns) is likely different from the reasons that students like them (less focus on memorization). Open-book tests, however, present complications. Bailey et al. (2020) notes that while students still needed a good level of understanding to succeed on open-book exams, these exams were best suited to higher-order subjects without a unique, searchable answer.

Changes were detected in the responses to the CAPE statements, “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course,” noted in Table 3 . Although there were statistically significant changes detected by the Mann–Whitney U Test, the means between Fall 2019 and Fall 2020 are still similar and positive. The results from this table indicate that students felt that there was not a decrease in learning and interest in their material. This might be due to instructors changing the design of assessments and assignments to accommodate for academic integrity and modality circumstances in the online learning format. The consistently positive CAPE ratings are also likely due to the fact that students are aware that CAPEs are an important factor for the departments’ hiring and retention decisions for faculty, and subsequently important for their instructors’ careers. Students may have also recognized that most of the difficulties in the switch to online learning were not the instructors’ fault. Students’ sympathy for the challenges that instructors faced may be contributing to the slightly more positive reviews during Fall 2020.

One of the most common experiences reported by students was a decrease in interaction with peers, with a strong majority of students saying that a lack of peer interaction hurt their learning experience. A study from Central Michigan University shows that peer interaction through in class activities supports optimal active learning ( Linton et al., 2014 ). Without face-to-face learning and asynchronous classes during COVID, instructors were not able to conduct the same collaborative activities. When asked how students interacted with their peers, the most common responses were student-run course forums or texting. This seems to support the findings of Wong (2020) which indicated that during ERT, students largely halted their use of synchronous forms of communication and opted instead for asynchronous ones, like instant messaging, with possible impacts on students’ social development. Students also reported a decrease in interaction with their instructors with a plurality saying that a lack of access to their instructors affected their academic experience. At the same time, ratings of professors’ ability to accommodate for the issues students faced during online education were high, as were students’ ratings of online office hours. It seems that students sympathized with instructors’ difficulties in the ERT transition but were aware that the lack of instructor presence impacted their learning experience nonetheless.

Limitations

There are some limitations in this study that should be considered before generalizing the results more widely. The survey was conducted at just a single university, UCSD: a large, highly-ranked, public research institution in the United States with its own unique approach to the COVID-19 pandemic. These results would likely differ significantly for online education at other universities. In addition, though care was taken to distribute the survey in channels used by all students, the voluntary response of students chosen from these channels does not constitute a simple random sample of undergraduates attending this institution. For example, our survey over-represents female students, who constituted 72.7% of the survey sample. The channels chosen could also bias certain results; for example, it is possible that students who answer online surveys released on the institution’s social media channels are less likely to have technical or Internet difficulties. Results from the small survey might be skewed slightly because respondents had to recall a year prior to their experiences in Fall 2019, whereas they might have had a more accurate memory of their Fall 2020 experience. CAPEs are completed at the end of the quarter when their recollection of their experiences is fresh, so those reviews are likely less susceptible to this unconscious bias.

The issues with sampling are somewhat mitigated in the CAPE data, but these responses are not themselves without issue. CAPE reviews are still a voluntary survey, and therefore are not a random sample of undergraduates. In addition, some instructors use extra credit to incentivize students to participate in CAPEs if the class meets a threshold percentage of responses, which might skew the population of respondents. CAPE responses tend to be relatively generous and positive, with students rating instructors and educational quality much higher in CAPE reviews than in our survey. This is possibly because the CAPE forms make it easy for students to report the most positive ratings on every item without considering them individually. Additionally, students are aware that CAPEs have an impact on the department’s decisions to rehire instructors.

Teaching Implications

Online learning presented multiple challenges for instructors and students, illuminating areas to improve in higher education that were not recognized before the COVID-19 pandemic. A majority of students expressed their comfort in engaging with the Zoom chat and polling. Students might feel this way since they can ask and answer questions using the chat feature without disrupting the focus in class. Therefore, in both further online learning and in-person classes, instructors might be able to stimulate interaction by lowering the social barriers to asking and answering questions. Applications such as Backchannel Chat, Yo Teach!, and NowComment offer more features than Zoom or Google Meet to prevent fatigue and increase retention in-person or online ( LearnWeaver, 2014 ; Hong Kong Polytechnic University, 2018 ; Paul Allison, 2018 ).

At the same time, increased interactivity in lectures, especially if required, is not necessarily a panacea for engagement issues. For example, some professors might require students to turn on their cameras, increasing accountability and giving an incentive to visibly focus as if in an in-person classroom. However, Castelli and Sarvary (2021) found, as we did, that the majority of students in an introductory collegiate biology course kept their cameras off; students cited concerns about their appearance, other people being seen behind them, and weak internet connections as the most common reasons for not keeping cameras on. Not only are these understandable concerns, but they correlate with identity as well: Castelli and Sarvary found that both underrepresented minorities and women were more likely to indicate that they worried about cameras showing others their surroundings and the people behind them.

Prior to the COVID-19 pandemic, online learning was a choice. Our research demonstrates that online learning has a long way to go before it can be used in an equitable manner that creates an engaging environment for all students, but that instructors adapted well to ERT to ensure courses promoted the same level of learning. The sudden nature of remote learning during the COVID pandemic did not allow for instructors or institutions to research and promote the most engaging online learning resources. Students have widely varying opinions and experiences with their higher education online learning experience during the pandemic. Our data analysis shows that distance learning during the pandemic had a toll on attendance during live lecture and peer-instructor connection. The difference in expected grades from Fall 2019 to Fall 2020 indicates that students felt differently about their ability to succeed in their online classes. In addition, students had trouble managing work loads during online learning. We gathered that instructors could be using engagement strategies more often to match students’ enthusiasm for those strategies, such as chat features and polls. Despite the challenges of online learning highlighted, this research also presents evidence that online learning can be engaging for students with the right tools. Student reviews indicated similarity before and after the switch to online learning, including indicating that course assignments promoted learning and the material was intellectually stimulating. These results propose that the courses and professors, despite the modality switch and changes to teaching and assessment strategies, maintained the level of learning that students felt they were getting out of their course.

Data Availability Statement

The data supporting the conclusions of this article contains potentially identifiable information. The authors can remove this identifying information prior to sharing the data.

Author Contributions

BH contributed to this project through formal analysis, investigation, and writing. PN contributed to the project through formal analysis, investigation, visualization, and writing. LC contributed to conceptualization, resources, supervision, writing, review, and editing. SH-L contributed methodology, supervision, writing, review, and editing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to acknowledge the Qualcomm Institute Learning Academy for supporting this project.

Abrami, P. C., Bernard, R. M., Bures, E. M., Borokhovski, E., and Tamim, R. M. (2012). “Interaction in distance education and online learning: using evidence and theory to improve practice,” in The Next Generation of Distance Education , eds L. Moller and J. B. Huett (Boston, MA: Springer), 49–69. doi: 10.1016/j.nedt.2014.06.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Almarghani, E., and Mijatovic, I. (2017). Factors affecting student engagement in HEIs – it is all about good teaching. Teach. Higher Educ. 22, 940–956. doi: 10.1080/13562517.2017.1319808

CrossRef Full Text | Google Scholar

Bailey, T., Kinnear, G., Sangwin, C., and O’Hagan, S. (2020). Modifying closed-book exams for use as open-book exams. OSF Preprint] doi: 10.31219/osf.io/pvzb7

Banna, J., Lin, M.-F. G., Stewart, M., and Fialkowski, M. K. (2015). Interaction matters: strategies to promote engaged learning in an online introductory nutrition course. J. Online Learn. Teach. 11, 249–261.

Google Scholar

Beer, C., Clark, K., and Jones, D. (2010). “Indicators of engagement,” in Proceedings of the Curriculum, Technology & Transformation for An Unknown. Proceedings Ascilite Sydney , eds C. H. Steel, M. J. Keppell, P. Gerbic, and S. Housego (Sydney, SA).

Bond, M., and Bedenlier, S. (2019). Facilitating student engagement through educational technology: towards a conceptual framework. J. Interact. Media Educ. 2019:11.

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., and Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. Int. J. Educ. Technol. Higher Educ. 17:2.

Britt, M., Goon, D., and Timmerman, M. (2015). How to better engage online students with online strategies. College Student J. 49, 399–404.

Brooks, D. C., Grajek, S., and Lang, L. (2020). Institutional readiness to adopt fully remote learning. Educ. Rev.

Bryer, J., and Speerschneider, K. (2016). Likert: Analysis and Visualization Likert Items. R Package Version 1.3.5. Available online at: https://CRAN.R-project.org/package=likert (accessed August 2021).

Bundick, M., Quaglia, R., Corso, M., and Haywood, D. (2014). Promoting student engagement in the classroom. Teach. Coll. Rec. 116, 1–43.

Bülow, M. W. (2022). “Designing synchronous hybrid learning spaces: challenges and opportunities,” in Hybrid Learning Spaces. Understanding Teaching-Learning Practice , eds E. Gil, Y. Mor, Y. Dimitriadis, and C. Köppe (Cham: Springer), doi: 10.1007/978-3-030-88520-5_9

Cakir, H. (2013). Use of blogs in pre-service teacher education to improve student engagement. Comp. Educ. 68, 244–252. doi: 10.1016/j.compedu.2013.05.013

Cardall, S., Krupat, E., and Ulrich, M. (2008). Live lecture versus video-recorded lecture: are students voting with their feet? Acad. Med. 83, 1174–1178. doi: 10.1097/acm.0b013e31818c6902

Castelli, F. R., and Sarvary, M. A. (2021). Why students do not turn on their video cameras during online classes, and an equitable, and inclusive plan to encourage them to do so. Ecol. Evol. 11, 3565–3576. doi: 10.1002/ece3.7123

Chakraborty, M., and Nafukho, F. M. (2014). Strengthening student engagement: what do students want in online courses? Eur. J. Train. Dev. 38, 782–802. doi: 10.1108/ejtd-11-2013-0123

Chan, L., Way, K., Hunter, M., Hird-Younger, M., and Daswani, G. (2020). Equity and Online Learning Survey Results. Toronto, ON: University of Toronto.

Chapman, E. (2002). Alternative approaches to assessing student engagement rates. Practical Assess. Res. Eval. 8, 1–7.

Chatterjee, R., and Correia, A. (2020). Online students’ attitudes toward collaborative learning and sense of community. Am. J. Distance Educ. 34, 53–68. doi: 10.1080/08923647.2020.1703479

Coates, H. (2007). A model of online and general campus based student engagement. Assess. Eval. Higher Educ. 32, 121–141.

Courses not CAPEd for Winter 22 (2022). Course and Professor Evaluations (CAPE). Available online at: https://cape.ucsd.edu/faculty/CoursesNotCAPEd.aspx (Retrieved March 3, 2022).

Desmione, L. M., and Carlson Le Floch, K. (2004). Are we asking the right questions? using cognitive interviews to improve surveys in education research. Educ. Eval. Policy Anal. 26, 1–22. doi: 10.3102/01623737026001001

Dixson, M. D. (2010). Creating effective student engagement in online courses: what do students find engaging? J. Scholarship Teach. Learn. 10, 1–13.

Fadde, P. J., and Vu, P. (2014). Blended online learning: benefits, challenges, and misconceptions. Online learn. Common Misconceptions Benefits Challenges 2014, 33–48. doi: 10.4018/978-1-5225-8009-6.ch002

Fitzgibbons, L., Kruelski, N., and Young, R. (2021). Breakout Rooms in an E-Learning Environment. Rochester, NY: University of Rochester Research.

Fredricks, J. A., Blumenfeld, P. C., and Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74, 59–109. doi: 10.3102/00346543074001059

Fredricks, J. A., Filsecker, M., and Lawson, M. A. (2016). Student engagement, context, and adjustment: addressing definitional, measurement, and methodological issues. Learn. Instruct. 43, 1–4.

Gaytan, J., and McEwen, B. C. (2007). Effective online instructional and assessment strategies. Am. J. Distance Educ. 21, 117–132. doi: 10.1080/08923640701341653

Gillett-Swan, J. (2017). The challenges of online learning: supporting and engaging the isolated learner. J. Learn. Design 10, 20–30. doi: 10.5204/jld.v9i3.293

Harrell, I. (2008). Increasing the success of online students. Inquiry: J. Virginia Commun. Colleges 13, 36–44.

Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., and Bond, M. A. (2020). The Difference Between Emergency Remote Teaching and Online Learning. EDUCAUSE Review.

Hong Kong Polytechnic University (2018). YoTeach!. Hong Kong: Hong Kong Polytechnic University.

Hussein, E., Daoud, S., Alrabaiah, H., and Badawi, R. (2020). Exploring undergraduate students’ attitudes towards emergency online learning during COVID-19: a case from the UAE. Children Youth Services Rev. 119:105699. doi: 10.1016/j.childyouth.2020.105699

Inside Higher Ed (2020). Responding to the COVID-19 Crisis: A Survey of College, and University Presidents. Inside Higher Ed: Washington, DC.

Johnson, N., Veletsianos, G., and Seaman, J. (2020). U.S. faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 Pandemic. Online Learn. 24, 6–21. doi: 10.24059/olj.v24i2.2285

Kahu, R. (2013). Framing student engagement in higher education. Stud. Higher Educ. 38, 758–773. doi: 10.1080/03075079.2011.598505

Kendricks, K. D. (2011). Creating a supportive environment to enhance computer based learning for underrepresented minorities in college algebra classrooms. J. Scholarsh. Teach. Learn. 12, 12–25.

Lear, J. L., Ansorge, C., and Steckelberg, A. (2010). Interactivity/community process model for the online education environment. J. Online Learn. Teach. 6, 71–77.

LearnWeaver (2014). Backchannel Chat Benefits. https://backchannelchat.com/Benefits

Lederman, D. (2020). How Teaching Changed in the (Forced) Shift to Remote Learning. How professors Changed Their Teaching in this Spring’s Shift to Remote Learning. Available online at: https://www.insidehighered.com/digital-learning/article/2020/04/22/how-professors-changed-their-teaching-springs-shift-remote (accessed April 22, 2020).

Levin, S., Whitsett, D., and Wood, G. (2013). Teaching MSW social work practice in a blended online learning environment. J. Teach. Soc. Work 33, 408–420. doi: 10.1080/08841233.2013.829168

Linton, D. L., Farmer, J. K., and Peterson, E. (2014). Is peer interaction necessary for optimal active learning? CBE—Life Sci. Educ. 13, 243–252. doi: 10.1187/cbe.13-10-0201

Liu, X., Magjuka, R., Bonk, C., and Lee, S. (2007). Does sense of community matter? an examination of participants’ perceptions of building learning communities in online courses. Quarterly Rev. Distance Educ. 8:9.

Ma, J., Han, X., Yang, J., and Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: the role of the instructor. Int. Higher Educ. 24, 26–34. doi: 10.1016/j.iheduc.2014.09.005

Mandernach, B. J. (2015). Assessment of student engagement in higher education: a synthesis of literature and assessment tools. Int. J. Learn. Teach. Educ. Res. 12, 1–14. doi: 10.1080/02602938.2021.1986468

Mandernach, B. J., Gonzales, R. M., and Garrett, A. L. (2006). An examination of online instructor presence via threaded discussion participation. J. Online Learn. Teach. 2, 248–260.

Martin, F., and Bolliger, D. U. (2018). Engagement matters: student perceptions on the importance of engagement strategies in the online learning environment. Online Learn. 22, 205–222. doi: 10.1186/s12913-016-1423-5

Means, B., and Neisler, J. (2020). Suddenly Online: A National Survey of Undergraduates During the COVID-19 Pandemic. San Mateo, CA: Digital Promise.

Moore, M. (1993). “Three types of interaction,” in Distance Education: New Perspectives , eds K. Harry, M. John, and D. Keegan (New York, NY: Routledge), 19–24.

Nelson Laird, T., and Kuh, D. (2005). Student experiences with information technology and their relationship to other aspects of student engagement. Res. Higher Educ. 46, 211–233. doi: 10.5811/westjem.2017.9.35163

Nicholson, S. (2002). Socializing in the “virtual hallway”: instant messaging in the asynchronous web-based distance education classroom. Int. Higher Educ. 5, 363–372.

Northey, G., Govind, R., Bucic, T., Chylinski, M., Dolan, R., and van Esch, P. (2018). The effect of “here and now” learning on student engagement and academic achievement. Br. J. Educ. Technol. 49, 321–333. doi: 10.1111/bjet.12589

Paul Allison (2018). NowComment for Educational Use. Mermaid Beach, QLD: Nowcomment.

Perets, E. A., Chabeda, D., Gong, A. Z., Huang, X., Fung, T. S., Ng, K. Y., et al. (2020). Impact of the emergency transition to remote teaching on student engagement in a non-stem undergraduate chemistry course in the time of covid-19. J. Chem. Educ. 97, 2439–2447. doi: 10.1021/acs.jchemed.0c00879

Quin, D. (2017). Longitudinal and contextual associations between teacher–student relationships and student engagement. Rev. Educ. Res. 87, 345–387. doi: 10.1016/j.jsp.2019.07.012

R Core Team (2020). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.

Rae, M. G., and McCarthy, M. (2017). The impact of vodcast utilisation upon student learning of physiology by first year graduate to entry medicine students. J. Scholarship Teach. Learn. 17, 1–23. doi: 10.14434/josotl.v17i2.21125

Raes, A., Detienne, L., and Depaepe, F. (2019). A systematic review on synchronous hybrid learning: gaps identified. Learn. Environ. Res. 23, 269–290. doi: 10.1007/s10984-019-09303-z

Read, D. L. (2020). Adrift in a Pandemic: Survey of 3,089 Students Finds Uncertainty About Returning to College. Toronto, ON: Top Hat.

Redmond, P., Heffernan, A., Abawi, L., Brown, A., and Henderson, R. (2018). An online engagement framework for higher education. Online Learn. 22, 183–204.

Reich, J., Buttimer, C., Fang, A., Hillaire, G., Hirsch, K., Larke, L., et al. (2020). Remote learning guidance from state education agencies during the COVID-19 pandemic: a first look. EdArXiv [Preprint]. doi: 10.35542/osf.io/437e2

Revere, L., and Kovach, J. V. (2011). Online technologies for engaged learning: a meaningful synthesis for educators. Quar. Rev. Distance Educ. 12, 113–124.

Rovai, A., and Wighting, M. (2005). Feelings of alienation and community among higher education students in a virtual classroom. Int. Higher Educ. 8, 97–110. doi: 10.1016/j.iheduc.2005.03.001

RStudio Team (2020). RStudio: Integrated Development for R. Boston, MA: RStudio.

Senn, S., and Wessner, D. R. (2021). Maintaining student engagement during an abrupt instructional transition: lessons learned from COVID-19. J. Microbiol. Biol. Educ. 22:22.1.47. doi: 10.1128/jmbe.v22i1.2305

Shea, P., Fredericksen, E., Pickett, A., Pelz, W., and Swan, K. (2001). Measures of learning effectiveness in the SUNY Learning Network. Online Educ. 2, 31–54.

Shin, M., and Hickey, K. (2020). Needs a little TLC: examining college students’ emergency remote teaching and learning experiences During covid-19. J. Further Higher Educ. 45, 973–986. doi: 10.1080/0309877x.2020.1847261

Shin, M., and Hickey, K. (2021). Needs a little TLC: examining college students’ emergency remote teaching and learning experiences during COVID-19. J. Furth. High. Educ. 45, 973–986.

Sumuer, E. (2018). Factors related to college students’ self-directed learning with technology. Australasian J. Educ. Technol. 34, 29–43. doi: 10.3389/fpsyg.2021.751017

Swan, K., and Shih, L. (2005). On the nature and development of social presence in online course discussions. J. Asynchronous Learn. Networks 9, 115–136.

Tess, P. A. (2013). The role of social media in higher education classes (real and virtual) – a literature review. Comp. Hum. Behav. 29, A60–A68.

UNESCO (2020). UNESCO Rallies International Organizations, Civil Society and Private Sector Partners in a Broad Coalition to Ensure #learningneverstops [Press Release]. Paris: UNESCO.

University of California San Diego [UCSD] (2021b). Transfer Students. Undergraduate Admissions. La Jolla, CA: UCSD.

University of California San Diego [UCSD] (2021a). Response Rate. Course and Professor Evaluations (CAPE). La Jolla, CA: UCSD.

University of California, San Diego Institutional Research (2021). Student Profile 2020-2021. La Jolla, CA: UCSD.

Venton, B. J., and Pompano, R. R. (2021). Strategies for enhancing remote student engagement through active learning. Anal. Bioanal. Chem. 413, 1507–1512. doi: 10.1007/s00216-021-03159-0

Vu, P., and Fadde, P. (2013). When to talk, when to chat: student interactions in live virtual classrooms. J. Interact. Online Learn. 12, 41–52.

Walker, K. A., and Koralesky, K. E. (2021). Student and instructor perceptions of engagement after the rapid online transition of teaching due to COVID-19. Nat. Sci. Educ. 50:e20038. doi: 10.1002/nse2.20038

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag.

Wimpenny, K., and Savin-Baden, M. (2013). Alienation, agency and authenticity: a synthesis of the literature on student engagement. Teach. Higher Educ. 18, 311–326. doi: 10.1080/13562517.2012.725223

Wong, R. (2020). When no one can go to school: does online learning meet students’ basic learning needs? Interact. Learn. Environ. 1–17.

Xiao, J. (2017). Learner-content interaction in distance education: the weakest link in interaction research. Distance Educ. 38, 123–135. doi: 10.1080/01587919.2017.1298982

Yildiz, S. (2009). Social presence in the web-based classroom: implications for intercultural communication. J. Stud. Int. Educ. 13, 46–65. doi: 10.1177/1028315308317654

Zepke, N., and Leach, L. (2010). Improving student engagement: ten proposals for action. Act. Learn. Higher Educ. 11, 167–177. doi: 10.1111/jocn.15810

Zhu, E. (2006). Interaction and cognitive engagement: an analysis of four asynchronous online discussions. Instruct. Sci. 34, 451–480.

Keywords : student engagement, undergraduate, online learning, in-person learning, remote instruction and teaching

Citation: Hollister B, Nair P, Hill-Lindsay S and Chukoskie L (2022) Engagement in Online Learning: Student Attitudes and Behavior During COVID-19. Front. Educ. 7:851019. doi: 10.3389/feduc.2022.851019

Received: 08 January 2022; Accepted: 11 April 2022; Published: 09 May 2022.

Reviewed by:

Copyright © 2022 Hollister, Nair, Hill-Lindsay and Chukoskie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Brooke Hollister, [email protected]

† These authors share first authorship

March 13, 2020

Online Learning during the COVID-19 Pandemic

What do we gain and what do we lose when classrooms go virtual?

By Yoshiko Iwai

Watchara Piriaputtanapun Getty Images

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

I woke up an hour late Wednesday morning, and by the time I had thrown on a sweatshirt, prepared my glass of Emergen-C, and logged onto Zoom , my class had been going on for 15 minutes. The night before I had taken cough syrup for my seasonal cold, and this was the first day my school switched to virtual instruction. Over the course of the three-hour workshop, I noticed my puffy eyes on the panel of faces and became self-conscious. I turned off my video. I became distracted with the noise of sirens outside and muted my speaker, only to then realize: by the time you’re done muting-and-unmuting, the right moment to join the conversation has already passed. I found myself texting on my computer, stepping away to make coffee, running to the bathroom, writing a couple e-mails, and staring at my classmate’s dog in one of the video panels. I don’t think my experience is unique; I imagined similar situations playing out in virtual offices and classrooms across the world.

In the aftermath of the World Health Organization’s designation of the novel coronavirus as a pandemic on March 11, universities across America are shutting down in an attempt to slow its spread. On March 6, the University of Washington took the lead , canceling all in-person classes, with a wave of universities across the country following suit: University of California, Berkeley, U.C., San Diego, Stanford, Rice, Harvard, Columbia, Barnard, N.Y.U, Princeton and Duke, among many others .

This shift into virtual classrooms is the culmination of the past weeks’ efforts to prevent COVID-19 from entering university populations and spreading to local communities: cancellation of university-funded international travel for conferences , blanket bans on any international travel for spring break, canceling study-abroad programs, creating registration systems for any domestic travel.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

Columbia University, which I now attend virtually, moved all classes online starting on March 11. The following morning, president Bollinger declared that classes would be held virtually for the remainder of the school year, and suspended all university-related travel; both international and domestic. The pandemic has affected over 114 countries, killing over 4,000 and shows no sign of abating, leading to chaos in university administration and among students. I find myself obsessing over my family in Japan, especially my mother, whose lung cancer puts her at particular risk. Cancellations are affecting future students as well—admitted students’ events, open houses, and campus tours are all being canceled to minimize contagion.

The quick turn to platforms like Zoom is disrupting curricula, particularly for professors less equipped to navigate the internet and the particularities of managing a classroom mediated by a screen and microphone. I had professors cancel class because they had technical difficulties, trouble with WiFi, or were simply panicked over the prospect of teaching the full class over the new platform. With university IT services focusing efforts on providing professors with how-to webinars on using online platforms, individual student needs for these same services have been placed on hold.

While the initial shift online has created a flurry of chaos, there are benefits to a virtual classroom. Especially in a place like New York, students can continue participating in discussion sections and lectures without riding the subway for an hour, avoiding the anxiety of using public transit or being in other incubators like classrooms, public bathrooms and cafeterias. Students can “sit in” on a class while nursing a common cold or allergies that come with the season, but which can make students a target of serious threats or violence—particularly racialized harassment for Asians. I have found immense relief in not having to pay for Lyfts to campus, avoiding side-eyes for my runny nose or using the little remaining hand sanitizer I have left after holding subway poles. In some situations, online teaching may not even affect student behavior or learning. Studies have shown that medical students learn and perform equally in live versus recorded lectures, and these results are reassuring at a time like the COVID-19 outbreak.

However, the reality is that some subjects are much harder to transfer online. A biochemistry or introductory economics lecture is easier to teach virtually than a music or dance class. The creation of a film or theatrical production requires physical bodies in close proximity. Even in my creative writing workshop, responding to a colleagues’ memoir about her mother’s death is hard to do without looking her in the eye. The screen creates an emotional remove that makes it difficult to have back-and-forth dialogue between multiple people, and it’s almost impossible to provide thoughtful feedback without feeling like you’re speaking into a void.

Over the last few decades, online learning in higher education has been studied extensively. Online MBA programs are on the rise, perhaps unsurprising for a field that often requires virtual conferencing and remote collaboration. Universities now offer online master’s programs to accommodate full-time work and long commutes, or to circumvent the financial barriers of moving to a new location with family. Online bachelor’s degrees are offered by a growing number of schools: Ohio State, University of Illinois Chicago, University of Florida, Arizona State, Penn State and many more. The benefits are the same: classes can be taken anywhere, lack of commute offers more time for studying or external commitments, and the structure is more welcoming to students with physical disability or illness. And yet, online learning hasn’t threatened the traditional model of in-person learning.

A large part of this can be attributed to accountability . Online classes require significantly more motivation and attention. I found it difficult to focus on a pixelated video screen when I could browse the internet on my computer, text on my phone, watch TV in the background, have one hand in the pantry, or just lay comfortably in my bed. The problem, too, is that webinar technology doesn’t quite live up to the hype. Noise and feedback—rustling papers, ambulances, kettles, wind—make it impossible to hear people talk, and so everyone is asked to mute their microphones.

But muting your audio means you can’t jump into a conversation quickly. The “raise hand” function often goes unnoticed by teachers and the chat box is distracting. Sometimes the gallery view just doesn’t work, so you’re stuck staring at your own face or just two of your eighteen classmates. It also means another hurdle for those who hesitate to speak up, even in the best of circumstances. It means you’re just one click away from turning off your camera and being totally off the hook. In an online class over the summer, I once watched a woman—who forgot her camera was still on, though she was muted—vacuum her entire kitchen and living room during a seminar.

In a recent New York Times article, columnist Kevin Roose wrote about his experience working from home while quarantined after COVID-19 exposure. Roose, once a remote worker, cites studies that suggest remote employees are more productive , taking shorter breaks and fewer sick days. But he also writes extensively about the isolation and lack of productivity he feels: “I’ve realized that I can’t be my best, most human self in sweatpants, pretending to pay attention on video conferences between trips to the fridge.” He notes that Steve Jobs, who was a firm believer in in-person collaboration and opposed remote work, once said, “Creativity comes from spontaneous meetings, from random discussions. You run into someone, you ask what they’re doing, you say ‘Wow,’ and soon you’re cooking up all sorts of ideas.”

In educational settings, creativity is arguably one of the most important things at stake. The surprises and unexpected interactions fuel creativity—often a result of sitting in a room brushing shoulders with a classmate, running into professors in a bathroom line, or landing on ideas and insights that arise out of discomfort in the room. This unpredictability is often lost online.

In the essay “Sim Life,” from her book, Make It Scream, Make it Burn , Leslie Jamison writes about the shortcomings of virtual life: “So much of lived experience is composed of what lies beyond our agency and prediction, beyond our grasp, in missteps and unforeseen obstacles and the textures of imperfection: the grit and grain of a sidewalk with its cigarette butts and faint summer stench of garbage and taxi exhaust, the possibility of a rat scuttling from a pile of trash bags, the lilt and laughter of nearby strangers’ voices.”

Classrooms offer these opportunities for riffs and surprise, and a large part of being a student is learning to deliver critique through uncomfortable eye contact, or negotiating a room full of voices and opinions that create friction with your own. When I Zoomed into class from my apartment, I missed being interrupted by classmates who complicated my ideas about a poem or short story. I missed being in workshop and bouncing ideas off of each other to find the best structure for a piece. I missed handwritten critiques, and felt limited in Word: no check pluses, no smiley faces, “Wow” feels flat when it’s not handwritten in the margins, and "Great" feels sarcastic in 10-point Calibri. I was frustrated that I could sleep in because online class meant I could wake up five minutes before class and pretend like I’d been ready all morning.

The COVID-19 pandemic will likely continue presenting challenges beyond those that come up in the course of routine virtual education. Even if this viral spread subsides, or a vaccination becomes readily available, the shift from online classes back to in-person learning may create disruptions of its own—adjusting back to higher standards of accountability, weaning off of phone-checking habits, and transferring comments back to hard copies instead of digital notes. Hopefully, these phases of trouble shooting can provide universities, professors and students the opportunity to practice adaptability, patience and resilience. And hopefully, these experiences will serve as preparation for future challenges that come with the next epidemic, pandemic and other disaster.

For now, I am trying to not look at myself in the gallery of faces, stop being distracted by my expressions, resisting the impulses to check my phone or e-mail, or at least recognize these urges when they arise.

Read more about the coronavirus outbreak  here .

• Share full article

Advertisement

Supported by

Student Opinion

Is Online Learning Effective?

A new report found that the heavy dependence on technology during the pandemic caused “staggering” education inequality. What was your experience?

By Natalie Proulx

During the coronavirus pandemic, many schools moved classes online. Was your school one of them? If so, what was it like to attend school online? Did you enjoy it? Did it work for you?

In “ Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says ,” Natasha Singer writes:

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution. In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home. Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.” The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner. The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States. The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said. “Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said. The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefited students before introducing them for educational use. Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed. “The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ” Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.” ​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching. “The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

Students, read the entire article and then tell us:

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .

The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

Related Content

Megan Kuhfeld, Jim Soland, Beth Tarasawa, Angela Johnson, Erik Ruzek, Karyn Lewis

December 3, 2020

Lindsay Dworkin, Karyn Lewis

October 13, 2021

Education Policy K-12 Education

Governance Studies

Brown Center on Education Policy

Douglas N. Harris

June 6, 2024

Phillip Levine, Luke Pardue

June 5, 2024

Carly D. Robinson, Katharine Meyer, Susanna Loeb

June 4, 2024

• Open access
• Published: 06 June 2024

Perceptions and challenges of online teaching and learning amidst the COVID-19 pandemic in India: a cross-sectional study with dental students and teachers

• Lakshmi Nidhi Rao 1 ,
• Aditya Shetty 1 ,
• Varun Pai 2 ,
• Srikant Natarajan 3 ,
• Manjeshwar Shrinath Baliga 4 ,
• Dian Agustin Wahjuningrum 5 ,
• Heeresh Shetty 6 ,
• Irmaleny Irmaleny 7 &
• Ajinkya M. Pawar 6  

BMC Medical Education volume  24 , Article number:  637 ( 2024 ) Cite this article

121 Accesses

Metrics details

Online education has emerged as a crucial tool for imparting knowledge and skills to students in the twenty-first century, especially in developing nations like India, which previously relied heavily on traditional teaching methods.

This study delved into the perceptions and challenges experienced by students and teachers in the context of online education during the COVID-19 pandemic. Data were collected from a sample of 491 dental students and 132 teachers utilizing a cross-sectional research design and an online-validated survey questionnaire.

The study’s findings revealed significant insights. Internet accessibility emerged as a major impediment for students, with online instruction proving more effective for theoretical subjects compared to practical ones. Although most teachers expressed comfort with online teaching, they highlighted the absence of classroom interaction as a significant challenge.

This study comprehensively examines the perspectives of both students and teachers regarding online education during the pandemic. The results carry substantial implications for the academic community, underscoring the need to address internet access issues and explore ways to enhance engagement and interaction in online learning environments.

Peer Review reports

Introduction

The COVID-19 pandemic has undeniably reshaped the global educational landscape, forcing a rapid shift towards online learning methodologies. While some disciplines have transitioned relatively smoothly, dental education presents unique challenges. Unlike fields with a primarily theoretical foundation, dental education hinges on the development of practical skills and direct patient interaction [ 1 , 2 ]. This inherent need for hands-on clinical experience necessitates a critical examination of online learning’s suitability for dental education [ 1 ].

Research across diverse international contexts underscores the limitations of online learning alone in fostering essential technical skills in dentistry [ 3 , 4 ]. Recognizing this reality, the Indian dental education model prioritizes hands-on learning as a core curricular element. However, the pre-clinical phases often incorporate simulations using mannequins, hinting at the potential for blended learning approaches. In this scenario, online platforms could be strategically utilized to deliver theoretical knowledge, thereby freeing up valuable classroom time for instructors to conduct in-person skill development sessions with students [ 5 , 6 ].

Despite advancements in technology, digitalization efforts in the Indian dental sector have primarily focused on practical training tools like computer-aided design/computer-aided manufacturing (CAD/CAM) and 3D printing technologies [ 7 , 8 ]. Traditional face-to-face lectures remained the dominant method for knowledge delivery, with online learning remaining largely unexplored within the Indian dental education curriculum before the pandemic [ 9 ].

The COVID-19 pandemic has disrupted this status quo, propelling online learning to the forefront of dental education [ 10 ]. This unprecedented situation necessitates a comprehensive assessment of its impact on the perceptions and experiences of both dental students and educators across India. This leads us to our central research question: “To what extent has the COVID-19 pandemic impacted the perceptions and challenges of online learning among dental students and teachers in India?”

By delving into this question, we aim to shed light on the strengths, weaknesses, and areas for improvement in online learning within the context of Indian dental education. These findings will inform future curricular development, allowing for a well-considered and strategic integration of online and traditional approaches. Ultimately, this research seeks to enhance the overall educational experience for dental students. By ensuring a balanced curriculum that leverages the strengths of both online and offline learning, we can equip future dentists with the essential knowledge and practical skills necessary to thrive in a rapidly evolving healthcare landscape.

Study design

The study utilized a cross-sectional research design to collect data from 500 dental students and 150 teachers in India. An online-validated survey questionnaire was employed to gather quantitative data. The study population consisted of undergraduate and postgraduate dental students and teachers from diverse dental colleges across India. Participants were selected through purposive sampling based on their willingness and availability during the study period. Ethical principles were strictly followed, including obtaining informed consent, ensuring confidentiality of participant data, and safeguarding participant privacy. This sampling method was chosen due to practical reasons, as randomly sampling would have been resource intensive. Leveraging existing networks and professional contacts facilitated access to a varied participant pool, ensuring engagement and data quality. To enhance representativeness, participants from various dental colleges, urban and rural locations, academic levels, and age groups were included in the sample.

Questionnaire

A self-administered, English-language questionnaire developed using Google Forms was utilized to evaluate perceptions and challenges of online dental education during the COVID-19 pandemic in India [ 11 ]. The questionnaire was structured around three main domains: satisfaction with online teaching, encountered problems, and comparisons between online and traditional classroom learning experiences.

In order to ensure the validity and reliability of this questionnaire within the unique context of Indian dental education, a thorough validation process was undertaken. Face validity was established through evaluation by a qualified researcher and questionnaire design specialist. Their assessment focused on the clarity, comprehensiveness, and relevance of the questions, resulting in revisions to improve clarity and minimize ambiguity in terminology, phrasing, and structure.

Content validity was ensured through the input of two subject-matter experts (SMEs) with significant experience in Indian dental education. These SMEs, who were independent of the study, assessed the questionnaire against the defined research objectives. Their feedback ensured that the questionnaire comprehensively covered the intended constructs, leading to further refinements.

Pilot testing was then conducted with a representative sample of 20 dental students and 10 teachers. This phase aimed to identify and address any remaining issues with the questionnaire’s understandability, flow, and length. Based on the feedback received from the pilot test participants, minor adjustments were made to optimize the user experience.

Data analysis

Following data collection, survey responses were entered into a Microsoft Excel spreadsheet and then imported into the Statistical Package for Social Sciences (SPSS) version 25 for analysis. Descriptive statistics were employed to summarize participant characteristics such as age, course of study (undergraduate, postgraduate), place of study (town, village), and self-reported familiarity with e-learning skills. These characteristics were presented as frequencies (N) and percentages (%) to provide an overview of the sample composition.

Chi-square tests were conducted to assess potential associations between categorical variables. However, the use of the Chi-square test is contingent upon meeting the assumption of expected cell frequencies being greater than 5. In instances where expected cell frequencies fell below 5, Fisher’s exact test was employed as a more appropriate alternative. Statistical significance was established at a p -value of 0.05 or less.

Participant characteristics and survey completion

A total of 500 students initiated the online survey, with a completion rate of 81.8% ( n  = 409). Similarly, among the 150 teachers who began the survey, 132 completed it (completion rate: 88%). To ensure a sufficient sample size for analysis, the survey period was extended beyond its original timeframe, potentially introducing a selection bias. This decision aligns with the purposive sampling methodology employed in this study.

Student perceptions

Satisfaction with online learning.

A significant portion (44.7%, n  = 183) of students aged 18–21 reported satisfaction with online instruction. Interestingly, age did not significantly influence satisfaction levels. Undergraduates expressed higher satisfaction compared to other course levels ( p  = 0.001). Location also played a role, with students from both urban (41.8%, n  = 79) and rural areas (45.6%, n  = 73) reporting similar contentment levels ( p -value = 0.034). Notably, students with advanced e-learning skills reported significantly higher satisfaction ( p -value = 0.001).

Evaluation of specific aspects

Students across various age groups, locations, and course levels expressed satisfaction with the topics covered ( p  = 0.032 for undergraduate students, p  = 0.002 for those knowledgeable about e-learning) and the instructors’ efforts (particularly those aged 18–21, p  = 0.001, undergraduates, p  = 0.010, and students with e-learning skills, p  = 0.001). However, no significant difference was observed in self-reported understanding of the subject matter based on demographics or e-learning skills. Overall, students aged 18–21 (42.7%, p  = 0.001) and those with e-learning knowledge ( p  = 0.006) exhibited greater appreciation for the quality of teaching.

Engagement and flexibility

Among participants familiar with e-learning (specific number not provided), a significant proportion (42.9%, p -value of 0.019) felt they could effectively engage with instructors during and after online sessions, regardless of age, location, or course level. Additionally, a notable number of undergraduate students with e-learning skills ( p -values of 0.039 and 0.001, respectively) appreciated the flexibility of attending online classes at their convenience. Furthermore, 40.2% of participants with e-learning skills ( p -value = 0.054) found online learning beneficial, particularly for theoretical subjects lacking practical components. Notably, a majority of participants across demographics agreed that online teaching could be valuable for future mass education initiatives (data presented in Table  1 ).

Challenges with online learning

Despite some advantages, participants with e-learning skills (48.2%) also reported internet connectivity and speed issues. Slow internet hindered video streaming for students across all age groups ( p  = 0.005). Only 20.6% of participants with e-learning skills disagreed with this finding.

Interaction and collaboration

Except for those residing in rural and semi-urban areas ( p  = 0.022), participants did not report significant concerns about general interaction problems. However, challenges emerged regarding sound quality and group study. Poor internet connections caused sound issues for students above 21 years old (55%, p  = 0.02) and those without e-learning skills (27.7%, p  = 0.03). Similarly, joint or group study proved difficult for participants over 21 (55%) and those residing in rural areas (48.8%, p  = 0.025).

Subject suitability

A significant portion (41%) of participants unfamiliar with e-learning skills expressed concerns about the effectiveness of online learning for subjects like mathematics, accounting, and laboratory-based courses ( p  = 0.077). This suggests that students perceive these subjects as requiring a more hands-on or interactive approach that may be challenging to replicate in an online environment.

Learning environment

Across all demographics, a consistent trend emerged: most participants reported feelings of isolation and a lack of belonging when learning online (data presented in Table  2 ). This indicates that online learning environments may not adequately foster the sense of community and social interaction typically found in traditional classrooms. Students generally favoured classroom settings for the increased engagement and interaction with teachers and classmates, qualities perceived as lacking in online environments. This preference was further supported by students with limited e-learning skills (33.7%), who agreed that classroom learning was superior and considered online teaching/learning to be less beneficial ( p  = 0.045).

Impact on learning

The majority of participants believed online classes had minimal impact on developing students’ overall personalities and communication skills. Students with limited e-learning skills (50.6%) likened online learning to watching YouTube lectures ( p  = 0.061), implying a passive learning experience. This suggests online learning may not be as effective as traditional classroom settings in fostering these crucial soft skills.

Despite concerns about suitability and learning environment, a significant portion of participants, particularly undergraduates (42.7%, p  = 0.001), expressed satisfaction with the topics covered and the instructors’ efforts in the online environment. This highlights a potential disconnect between student concerns and their actual experience with well-designed online learning.

Most undergraduates strongly agreed (49.2%, p  = 0.04) that online teaching/learning is extremely useful during disasters such as the coronavirus pandemic (Table  3 ). This emphasizes the potential of online learning as a contingency measure for educational continuity during unforeseen circumstances.

Overall, student perceptions regarding the suitability and learning outcomes of online learning were mixed. While some found it beneficial for specific situations and expressed satisfaction with well-designed online courses, concerns existed about its effectiveness in fostering a sense of community, developing soft skills, and replicating the interactive nature of traditional classroom settings.

Teacher perceptions

Advantages of online teaching.

A considerable number of teachers (40%) viewed online classes as a more adaptable alternative to traditional classroom settings. Similarly, nearly half (49.2%) expressed this view regarding student accessibility. Additionally, a significant majority (59.8%) believed online teaching offered students improved 24/7 access to learning materials.

Challenges of online teaching

Teachers reported a significant decrease (50%) in the use of standardized coursework compared to traditional classrooms. While they strongly disagreed (38%) that online teaching eliminates the need for proper lesson planning, they overwhelmingly felt it hindered creating a good interactive environment with students (87%). Furthermore, teachers believed students were less likely to ask questions in an online setting (61%) compared to a physical classroom. However, most teachers (79%) appreciated the elimination of physical travel associated with online teaching.

Suitability and effectiveness

In terms of learner level, online teaching was perceived as more suitable for advanced learners (58%) than beginners. Teachers also believed online teaching was better suited for theory-based subjects (87%) compared to laboratory-based ones. Opinions were divided regarding the optimal use of online teaching for knowledge transfer, with 39% disagreeing and 24% remaining neutral. The teachers concurred that online teaching was a valuable tool during crises like the COVID-19 pandemic, but they generally preferred face-to-face teaching under normal circumstances. Data pertaining to these findings is presented in Table  4 .

The COVID-19 pandemic necessitated a rapid shift to online learning platforms in dental education globally, including India. While this transition aimed to maintain educational continuity [ 12 ], it presented unique challenges for a country grappling with limited internet infrastructure [ 13 ]. Existing disparities in access were exacerbated by the pandemic’s suddenness, highlighting the need for innovative solutions tailored to the Indian context [ 14 ].

Our study aimed to understand the perceptions and challenges of online dental education among students and educators. Our findings resonate with existing research, highlighting both the advantages and limitations of online learning. Similar to previous studies, both students and educators in our research acknowledged the benefits of flexibility, improved online teaching skills, and efficient time management [ 15 , 16 , 17 , 18 , 19 ]. Additionally, the significant role of online resources and social media platforms in fostering learning and interaction, as emphasized by Azer et al. (2023) and Wimardhani et al. (2023), was evident in our findings [ 17 , 19 ].

This research explored factors influencing student satisfaction with online learning. Consistent with Shaheen et al. (2023), our results indicated higher satisfaction among younger students (aged 18–21) and those with stronger e-learning skills, suggesting a correlation with technological comfort [ 18 ]. However, unlike Schlenz et al. (2023) who observed a general preference for online learning, our study did not find significant variations in satisfaction based on age, location, or field of study [ 15 ]. Notably, students with advanced e-learning skills reported higher dissatisfaction with internet connectivity and speed, suggesting a potential link between heightened expectations and increased frustration with technical limitations. This aligns with observations made by Pratheebha & Jayaraman (2022), Chang et al. (2021), and Wimardhani et al. (2023) regarding student challenges in online learning environments [ 16 , 19 , 20 ]. While acknowledging the quality of online instruction, many students in our study, similar to those in Chang et al. (2021), expressed feelings of isolation and a preference for the interactive elements of traditional classroom settings [ 20 ].

The transition to online learning presented specific challenges in dental education, particularly for subjects requiring hands-on experience. Deery (2020) emphasizes the need for dental schools to adapt their curricula and policies to incorporate effective distance learning methods [ 21 ]. Our research reinforces this notion by highlighting the importance of a strong educator-student connection for successful online learning. In the face of these challenges, educators and administrators remain committed to creating a conducive learning environment that prioritizes adaptability.

Online learning platforms offer unique advantages. E-learning technologies empower learners to personalize their learning pace, sequence, and content, leading to improved engagement [ 22 ]. Additionally, recorded online lectures provide flexibility for students to access learning materials at their convenience [ 23 ]. Our research, building upon prior work by Pham (2022) and Chang et al. (2021), demonstrated a weaker association between peer-to-peer interactions and student satisfaction, consistent with findings in other online learning environments [ 20 , 24 ].

Several factors influence the success of online education, including educator willingness to share content online, student capacity for online learning, and the quality of available digital resources [ 25 ]. Political, economic, and cultural factors also significantly influence the transition from traditional to online learning [ 25 ]. While acknowledging the potential for academic collaboration and remote work, many educators recognize the opportunity to integrate blended learning models into future curriculum development [ 26 ].

“Internet self-efficacy” – an individual’s confidence in navigating online tasks – plays a crucial role in online learning success [ 27 ]. In India, internet connectivity disparities between urban and rural areas present a challenge for both students and teachers. These connectivity issues, along with software problems and audio/video functionalities, can disrupt learning and create a frustrating experience. Institutions can mitigate these challenges by offering comprehensive internet skills training to enhance students’ and educators’ internet self-efficacy before implementing online courses [ 24 ]. However, the pandemic’s swift implementation of remote learning may have limited the availability of such training protocols.

Challenges and innovations in clinical skills development

While online learning offers numerous advantages, it presents unique challenges in dental education, particularly for subjects requiring hands-on clinical experience with patients. The absence of direct patient interaction remains a significant hurdle [ 21 ]. However, several institutions are actively addressing this limitation by adopting diverse e-learning tools like flash multimedia, digitized images, virtual patient simulations, and virtual reality (VR) simulators. Research has shown the effectiveness of these tools in teaching various clinical skills, including examination, palpation, surgical procedures, and resuscitation [ 28 ]. Notably, VR simulators have been found to be equally effective as live patient interactions in achieving learning objectives, offering a promising solution for overcoming limitations in online dental education.

The rise of virtual interaction and blended learning models

The COVID-19 pandemic has significantly transformed the educational landscape in dental education by introducing virtual teaching platforms. This shift has reshaped interactions between educators and students, impacting how they learn and assess progress. The rise of web-based resources has facilitated the emergence of innovative virtual interaction methods, such as student-patient simulations and peer mentoring programs. Research suggests these methods can be effective in enhancing medical students’ knowledge and psychological well-being [ 29 ]. However, this transition to online learning has also encountered obstacles, including technical difficulties, privacy concerns, reduced student engagement, and potential exacerbation of mental health issues due to social isolation [ 27 , 29 , 30 ].

Optimizing blended learning for future dental education

The unique circumstances of the COVID-19 pandemic have highlighted the importance of exploring student preferences and technical challenges to optimize blended learning models in dental education. By addressing the diverse needs of students and effectively integrating online and offline learning components, educators can foster successful learning outcomes in an ever-evolving educational environment [ 30 ]. This research underscores the multifaceted nature of online dental education and emphasizes the necessity for collaborative efforts to leverage its advantages while mitigating limitations.

Building educational resilience and adaptability

The significance of these studies extends beyond immediate pandemic adaptations. They contribute to a broader understanding of learning adaptations, hybrid learning environments, digital literacy, pedagogical innovation, mental health and well-being, policy implications, and the continuous enhancement of educational practices [ 30 ]. Reflecting on experiences and lessons learned during the pandemic can assist educational institutions in refining their teaching and learning approaches, ensuring greater resilience and adaptability in the face of future challenges [ 29 ]. Therefore, the insights from these studies offer valuable guidance for shaping the future of dental education and broader educational practices in a post-pandemic world.

Limitations and future research directions

We acknowledge limitations in our study. Employing random sampling methods in future research would be crucial to draw more widely applicable conclusions regarding perceptions and challenges in online dental education in India. Additionally, we recognize the challenges associated with relying on self-reported data, including potential social desirability bias. While acknowledging these limitations, our study adopted a people-centred approach, employing a diverse questionnaire, contextual analysis, and insightful techniques to gain a profound understanding of participants’ experiences with digital instruction. However, these limitations underscore the need for further exploration, particularly in understanding the potential misalignment between outcomes of digital and in-person events from instructors’ perspectives. This area warrants additional research through targeted interviews, subgroup analyses, and consideration of contextual factors, aiming to enhance our understanding of effective teaching modes and benefitting student learning outcomes.

In conclusion, the COVID-19 pandemic has accelerated the adoption of online and virtual teaching platforms in dental education, offering both opportunities and challenges. By exploring student preferences and addressing technical obstacles, educators can refine blended learning models to better cater to diverse student needs. The insights gleaned from pandemic experiences provide valuable direction for bolstering the resilience and adaptability of educational practices in a post-pandemic era.

Availability of data and materials

The datasets used and/or analysed throughout the current investigation are attainable from the corresponding author following a justifiable request.

Desai BK. Clinical implications of the COVID-19 pandemic on dental education. J Dent Educ. 2020;84:512.

Article   Google Scholar  

Alsoufi A, Alsuyihili A, Msherghi A, Elhadi A, Atiyah H, Ashini A, Ashwieb A, Ghula M, Ben Hasan H, Abudabuos S, Alameen H, Abokhdhir T, Anaiba M, Nagib T, Shuwayyah A, Benothman R, Arrefae G, Alkhwayildi A, Alhadi A, Zaid A, Elhadi M. Impact of the COVID-19 pandemic on medical education: medical students’ knowledge, attitudes, and practices regarding electronic learning. PLoS One. 2020;15: e0242905.

Hillenburg KL, Cederberg RA, Gray SA, Hurst CL, Johnson GK, Potter BJ. E-learning and the future of dental education: opinions of administrators and information technology specialists. Eur J Dent Educ. 2006;10:169–77.

Naik N, Hameed BMZ, Sooriyaperakasam N, Vinayahalingam S, Patil V, Smriti K, Saxena J, Shah M, Ibrahim S, Singh A, Karimi H, Naganathan K, Shetty DK, Rai BP, Chlosta P, Somani BK. Transforming healthcare through a digital revolution: a review of digital healthcare technologies and solutions. Front Digit Health. 2022;4: 919985.

Machado RA, Bonan PRF, Perez D, Martelli JÚnior H. COVID-19 pandemic and the impact on dental education: discussing current and future perspectives. Braz Oral Res. 2020;34:e083.

Talapko J, Perić I, Vulić P, Pustijanac E, Jukić M, Bekić S, Meštrović T, Škrlec I. Mental health and physical activity in health-related university students during the COVID-19 pandemic. Healthc (Basel). 2021;9:801.

Google Scholar  

Jum’ah AA, Elsalem L, Loch C, Schwass D, Brunton PA. Perception of health and educational risks amongst dental students and educators in the era of COVID-19. Eur J Dent Educ. 2021;25:506–15.

O’Doherty D, Dromey M, Lougheed J, Hannigan A, Last J, McGrath D. Barriers and solutions to online learning in medical education - an integrative review. BMC Med Educ. 2018;18:130.

Schlenz MA, Schmidt A, Wöstmann B, Kramer N, Schulz-Weidner N. Students’ and lecturers’ perspective on the implementation of online learning in dental education due to SARS-CoV-2 (COVID-19): a cross-sectional study. BMC Med Educ. 2020;20:354.

Röhle A, Horneff H, Willemer MC. Practical teaching in undergraduate human and dental medical training during the COVID-19 crisis. Report on the COVID19-related transformation of peer-based teaching in the Skills Lab using an Inverted Classroom Model. GMS J Med Educ. 2021;38:Doc2.

Wright KB. Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. J Computer-Mediated Communication. 2006;10:1034.

Turkyilmaz I, Hariri NH, Jahangiri L. Student\’s perception of the impact of e-learning on dental education. J Contemp Dent Pract. 2019;20:616–21.

Warnecke E, Pearson S. Medical students’ perceptions of using e-learning to enhance the acquisition of consulting skills. Australas Med J. 2011;4:300–7.

Tull S, Dabner N, Ayebi-Arthur K. Social media and e-learning in response to seismic events: resilient practices. Journal of Open, Flexible and Distance Learning. 2020;24:63–76.

Schlenz MA, Wöstmann B, Krämer N, Schulz-Weidner N. Update of students’ and lecturers’ perspectives on online learning in dental education after a five-semester experience due to the SARS-CoV-2 (COVID-19) pandemic: insights for future curriculum reform. BMC Med Educ. 2023;23(1):556. https://doi.org/10.1186/s12909-023-04544-2 .

Pratheebha C, Jayaraman M. Learning and satisfaction levels with online teaching methods among undergraduate dental students - a survey. J Adv Pharm Technol Res. 2022;13(Suppl 1):S168–72. https://doi.org/10.4103/japtr.japtr_285_22 . (Epub 2022 Nov 30).

Azer SA, Alhudaithi D, AlBuqami F, et al. Online learning resources and social media platforms used by medical students during the COVID-19 pandemic. BMC Med Educ. 2023;23:969. https://doi.org/10.1186/s12909-023-04906-w .

Shaheen MY, Basudan AM, Almubarak AM, Alzawawi AS, Al-Ahmari FM, Aldulaijan HA, Almoharib H, Ashri NY. Dental students’ perceptions towards e-learning in comparison with traditional classroom learning. Cureus. 2023;26: e51129. https://doi.org/10.7759/cureus.51129 .

Wimardhani YS, Indrastiti RK, Ayu AP, Soegyanto AI, Wardhany II, Subarnbhesaj A, Nik Mohd Rosdy NMM, Do TT. Perceptions of online learning implementation in dental education during the COVID-19 pandemic: a cross-sectional study of dental school faculty members in Southeast Asia. Dent J (Basel). 2023;11:article 201. https://doi.org/10.3390/dj11090201 .

Yu-Fong Chang J, Wang LH, Lin TC, Cheng FC, Chiang CP. Comparison of learning effectiveness between physical classroom and online learning for dental education during the COVID-19 pandemic. J Dent Sci. 2021;16:1281–9. https://doi.org/10.1016/j.jds.2021.07.016 .

Deery C. The COVID-19 pandemic: implications for dental education. Evid Based Dent. 2020;21:46–7.

Taylor DL, Yeung M, Bashet AZ. Personalized and adaptive learning. 2021. p. 17–34.

Tull S, Dabner N, Ayebi-Arthur K. Social media and e-learning in response to seismic events: resilient practices. J Open Flex Distance Learn. 2017;21:63–76.

Pham AT. Engineering students’ interaction in online classes via google meet: a case study during the COVID-19 pandemic. Int J Eng Pedagogy (iJEP). 2022;12:158–70.

Scanlon E, McAndrew P, O’Shea T. Designing for educational technology to enhance the experience of learners in distance education: how open educational resources, learning design and Moocs are influencing learning. J Interact Media Educ. 2015;2015:1–9.

Ong SGT, Quek GCL. Enhancing teacher–student interactions and student online engagement in an online learning environment. Learn Environ Res. 2023;26:681–707.

Hsu M-H, Chiu C-M. Internet self-efficacy and electronic service acceptance. Decis Support Syst. 2004;38:369–81.

Iyer P, Aziz K, Ojcius DM. Impact of COVID-19 on dental education in the United States. J Dent Educ. 2020;84:718–22.

Pokhrel S, Chhetri RA. Literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future. 2021;8:133–41. https://doi.org/10.1177/2347631120983481 .

Nurunnabi M, Almusharraf N, Aldeghaither D. Mental health and well-being during the COVID-19 pandemic in higher education: evidence from G20 countries. J Public Health Res. 2021;9:2010.

Download references

Acknowledgements

The authors are grateful to all the volunteers who participated in the study.

The authors did not receive support from any organizations for the submitted work.

Author information

Authors and affiliations.

Department of Conservative Dentistry and Endodontics, AB Shetty Memorial Institute of Dental Sciences, NITTE Deemed to be University, Mangalore, Karnataka, India

Lakshmi Nidhi Rao & Aditya Shetty

Forensic Medicine Unit, Faculty of Medicine, AIMST University, Jalan Semeling, Bedong, Kedah, Malaysia

Department of Oral Pathology and Microbiology, Manipal College of Dental Sciences, Mangalore, Karnataka, India

Srikant Natarajan

Research Unit, Mangarole Institute of Oncology, Pumpwell, Mangalore, Karnataka, 575002, India

Manjeshwar Shrinath Baliga

Department of Conservative Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia

Dian Agustin Wahjuningrum

Department of Conservative Dentistry and Endodontics, Nair Hospital Dental College, Mumbai, Maharashtra, 400008, India

Heeresh Shetty & Ajinkya M. Pawar

Department of Conservative Dentistry, Universitas Padjadjaran, Bandung, Indonesia

Irmaleny Irmaleny

You can also search for this author in PubMed   Google Scholar

Contributions

LNR: Conception and design of the study, Data acquisition, Data analysis, Discussion of the results, Drafting of the manuscript. AS: Conception and design of the study, Data acquisition, Discussion of the results, Drafting of the manuscript. VP: Conception and design of the study, Data acquisition, Data analysis, Discussion of the results, Drafting of the manuscript. SN: Conception and design of the study, Data acquisition, Data analysis, Discussion of the results, Drafting of the manuscript. MSB: Conception and design of the study, Data acquisition, Data analysis, Discussion of the results, Drafting of the manuscript. HS: Drafting of the manuscript, Proofreading and editing for final submission. AMP: Proofreading and editing for final submission. DAW: Proofreading and editing for final submission. II: Proofreading and editing for final submission. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Dian Agustin Wahjuningrum or Ajinkya M. Pawar .

Ethics declarations

Ethics approval and consent to participate.

The study was done after obtaining clearance from Institutional Ethics Committee (PEAIEC/2020/01/05).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Rao, L.N., Shetty, A., Pai, V. et al. Perceptions and challenges of online teaching and learning amidst the COVID-19 pandemic in India: a cross-sectional study with dental students and teachers. BMC Med Educ 24 , 637 (2024). https://doi.org/10.1186/s12909-024-05340-2

Download citation

Received : 18 October 2023

Accepted : 22 March 2024

Published : 06 June 2024

DOI : https://doi.org/10.1186/s12909-024-05340-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Coronavirus pandemic
• Face-to-face-instruction
• Online education
• Education and training
• Worldwide web technology

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Academic Affairs
• Partnerships/Mergers
• Personnel and HR
• Policy and Legal Affairs
• Fundraising
• Crisis Management
• Access & Affordability
• Diversity, Equity, Inclusion
• Student Loans/Pell Grants
• International Students
• Online Learning
• Instructional Technology
• Edtech/Innovation
• Civil Dialogue
• Curricula/Majors
• Mental health
• Career Pathways
• Campus Safety
• Student Success and Retention
• Dining and Retail
• Athletics/Recreation
• Web Seminars – UB Ed Talks – Podcasts

Is online learning still popular after the pandemic ended?

Although it existed before the pandemic, online learning took off with as many as 75% of college students enrolled in distance education courses in the fall of 2020. Is it still an option students prefer?

New data from Forbes Advisor reveals enrollment declines in online learning since 2020, although participation remains robust. In the fall of 2022, approximately 54% of college students took online classes, which represents about 10 million learners.

“Instead of reverting to the pre-pandemic status quo, college students increasingly choose to take classes online,” Forbes’ Ilana Hamilton wrote.

The state of online learning by the numbers

A fair share of college students prefer taking online courses exclusively, the data suggests. Twenty-six percent of students enrolled in online classes only in fall 2022. Sixty-three percent of those learners attended in-state colleges, while 33% enrolled in schools in other states.

Enrollment is similar when comparing undergraduate to graduate students. Fifty-four percent vs. 53.5% of undergrad and graduate students took distance education courses in fall 2022, respectively.

However, undergrads are more likely to prefer hybrid options with 30.5% taking both online and on-campus courses. Fully online options are more popular among grad students (38.7%).

More from UB : This branding agency ranks the 33 best college mottos

Finally, for-profit private colleges are far more likely to enroll students who exclusively take online classes. Here’s a look at that data:

• For-profit private colleges : 64.8% of all students enrolled in online only
• Nonprofit private colleges : 26.5% of all students enrolled in online only
• Public colleges : 23.6% of all students enrolled in online only

“Online learners save the most when choosing a private, nonprofit college,” the analysis reads. “While the median cost of tuition and fees at primarily online colleges in this category is around $10,000, the overall cost of tuition and fees reaches nearly $28,000, indicating that degree seekers interested in private, nonprofit institutions can cut costs with an online format.”

To explore more of the data, including stats about online learner demographics and degree type, click here .

Most Popular

This branding agency ranks the 33 best college mottos, most american universities plunge in this popular international ranking, 12 key traits of a successful higher education leader, “too good to be true:” how one it team unlocked millions..., with stopped out adults returning, where should you focus, credit for prior learning is showing value. should your college care.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

Jessie s. barrot.

College of Education, Arts and Sciences, National University, Manila, Philippines

Ian I. Llenares

Leo s. del rosario, associated data.

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

Literature review

Education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x ̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table ​ Table1 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Participants’ Online Learning Platforms

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

The extent of students’ online learning challenges

Table ​ Table2 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x ̅  = 2.62, SD  = 1.03) with scores ranging from x ̅  = 1.72 ( to some extent ) to x ̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x ̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x ̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x ̅  = 2.51, SD  = 1.31), SIC ( x ̅  = 2.77, SD  = 1.34), and LRC ( x ̅  = 2.93, SD  = 1.31).

The Extent of Students’ Challenges during the Interim Online Learning

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table ​ Table3, 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

Summary of students’ responses on the impact of COVID-19 on their online learning experience

Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table ​ Table4 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table ​ Table4 4 further reveals that strategies used by students within a specific type of challenge vary.

Students’ Strategies to Overcome Online Learning Challenges

Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Authors’ contributions

Jessie Barrot led the planning, prepared the instrument, wrote the report, and processed and analyzed data. Ian Llenares participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing. Leo del Rosario participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing.

No funding was received in the conduct of this study.

Availability of data and materials

Declarations.

The study has undergone appropriate ethics protocol.

Informed consent was sought from the participants.

Authors consented the publication. Participants consented to publication as long as confidentiality is observed.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Adarkwah MA. “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies. 2021; 26 (2):1665–1685. doi: 10.1007/s10639-020-10331-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Almaiah MA, Al-Khasawneh A, Althunibat A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies. 2020; 25 :5261–5280. doi: 10.1007/s10639-020-10219-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Araujo T, Wonneberger A, Neijens P, de Vreese C. How much time do you spend online? Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures. 2017; 11 (3):173–190. doi: 10.1080/19312458.2017.1317337. [ CrossRef ] [ Google Scholar ]
• Barrot, J. S. (2016). Using Facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29 (3), 286–301.
• Barrot, J. S. (2018). Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from 2010 to 2017. Journal of Computer Assisted Learning, 34 (6), 863–875.
• Barrot, J. S. (2020). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research . 10.1177/0735633120972010.
• Barrot, J. S. (2021). Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer Assisted Language Learning . 10.1080/09588221.2021.1883673.
• Bergen N, Labonté R. “Everything is perfect, and we have no problems”: Detecting and limiting social desirability bias in qualitative research. Qualitative Health Research. 2020; 30 (5):783–792. doi: 10.1177/1049732319889354. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Birks, M., & Mills, J. (2011). Grounded theory: A practical guide . Sage.
• Boelens R, De Wever B, Voet M. Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review. 2017; 22 :1–18. doi: 10.1016/j.edurev.2017.06.001. [ CrossRef ] [ Google Scholar ]
• Buehler MA. Where is the library in course management software? Journal of Library Administration. 2004; 41 (1–2):75–84. doi: 10.1300/J111v41n01_07. [ CrossRef ] [ Google Scholar ]
• Carter RA, Jr, Rice M, Yang S, Jackson HA. Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences. 2020; 121 (5/6):321–329. doi: 10.1108/ILS-04-2020-0114. [ CrossRef ] [ Google Scholar ]
• Cavanaugh CS, Barbour MK, Clark T. Research and practice in K-12 online learning: A review of open access literature. The International Review of Research in Open and Distributed Learning. 2009; 10 (1):1–22. doi: 10.19173/irrodl.v10i1.607. [ CrossRef ] [ Google Scholar ]
• Cicchetti D, Bronen R, Spencer S, Haut S, Berg A, Oliver P, Tyrer P. Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. The Journal of Nervous and Mental Disease. 2006; 194 (8):557–564. doi: 10.1097/01.nmd.0000230392.83607.c5. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Copeland WE, McGinnis E, Bai Y, Adams Z, Nardone H, Devadanam V, Hudziak JJ. Impact of COVID-19 pandemic on college student mental health and wellness. Journal of the American Academy of Child & Adolescent Psychiatry. 2021; 60 (1):134–141. doi: 10.1016/j.jaac.2020.08.466. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Day T, Chang ICC, Chung CKL, Doolittle WE, Housel J, McDaniel PN. The immediate impact of COVID-19 on postsecondary teaching and learning. The Professional Geographer. 2021; 73 (1):1–13. doi: 10.1080/00330124.2020.1823864. [ CrossRef ] [ Google Scholar ]
• Donitsa-Schmidt S, Ramot R. Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching. 2020; 46 (4):586–595. doi: 10.1080/02607476.2020.1799708. [ CrossRef ] [ Google Scholar ]
• Drane, C., Vernon, L., & O’Shea, S. (2020). The impact of ‘learning at home’on the educational outcomes of vulnerable children in Australia during the COVID-19 pandemic. Literature Review Prepared by the National Centre for Student Equity in Higher Education. Curtin University, Australia.
• Elaish M, Shuib L, Ghani N, Yadegaridehkordi E. Mobile English language learning (MELL): A literature review. Educational Review. 2019; 71 (2):257–276. doi: 10.1080/00131911.2017.1382445. [ CrossRef ] [ Google Scholar ]
• Fawaz, M., Al Nakhal, M., & Itani, M. (2021). COVID-19 quarantine stressors and management among Lebanese students: A qualitative study.  Current Psychology , 1–8. [ PMC free article ] [ PubMed ]
• Franchi T. The impact of the Covid-19 pandemic on current anatomy education and future careers: A student’s perspective. Anatomical Sciences Education. 2020; 13 (3):312–315. doi: 10.1002/ase.1966. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garcia R, Falkner K, Vivian R. Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education. 2018; 123 :150–163. doi: 10.1016/j.compedu.2018.05.006. [ CrossRef ] [ Google Scholar ]
• Gonzalez T, De La Rubia MA, Hincz KP, Comas-Lopez M, Subirats L, Fort S, Sacha GM. Influence of COVID-19 confinement on students’ performance in higher education. PLoS ONE. 2020; 15 (10):e0239490. doi: 10.1371/journal.pone.0239490. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hew KF, Jia C, Gonda DE, Bai S. Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education. 2020; 17 (1):1–22. doi: 10.1186/s41239-020-00234-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang Q. Comparing teacher’s roles of F2F learning and online learning in a blended English course. Computer Assisted Language Learning. 2019; 32 (3):190–209. doi: 10.1080/09588221.2018.1540434. [ CrossRef ] [ Google Scholar ]
• John Hopkins University. (2021). Global map . https://coronavirus.jhu.edu/
• Kapasia N, Paul P, Roy A, Saha J, Zaveri A, Mallick R, Chouhan P. Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India. Children and Youth Services Review. 2020; 116 :105194. doi: 10.1016/j.childyouth.2020.105194. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kebritchi M, Lipschuetz A, Santiague L. Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems. 2017; 46 (1):4–29. doi: 10.1177/0047239516661713. [ CrossRef ] [ Google Scholar ]
• Khalil R, Mansour AE, Fadda WA, Almisnid K, Aldamegh M, Al-Nafeesah A, Al-Wutayd O. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education. 2020; 20 (1):1–10. doi: 10.1186/s12909-020-02208-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Matsumoto K. Introspection, verbal reports and second language learning strategy research. Canadian Modern Language Review. 1994; 50 (2):363–386. doi: 10.3138/cmlr.50.2.363. [ CrossRef ] [ Google Scholar ]
• Ng W. Can we teach digital natives digital literacy? Computers & Education. 2012; 59 (3):1065–1078. doi: 10.1016/j.compedu.2012.04.016. [ CrossRef ] [ Google Scholar ]
• Pham T, Nguyen H. COVID-19: Challenges and opportunities for Vietnamese higher education. Higher Education in Southeast Asia and beyond. 2020; 8 :22–24. [ Google Scholar ]
• Rasheed RA, Kamsin A, Abdullah NA. Challenges in the online component of blended learning: A systematic review. Computers & Education. 2020; 144 :103701. doi: 10.1016/j.compedu.2019.103701. [ CrossRef ] [ Google Scholar ]
• Recker MM, Dorward J, Nelson LM. Discovery and use of online learning resources: Case study findings. Educational Technology & Society. 2004; 7 (2):93–104. [ Google Scholar ]
• Roblek V, Mesko M, Dimovski V, Peterlin J. Smart technologies as social innovation and complex social issues of the Z generation. Kybernetes. 2019; 48 (1):91–107. doi: 10.1108/K-09-2017-0356. [ CrossRef ] [ Google Scholar ]
• Seplaki CL, Agree EM, Weiss CO, Szanton SL, Bandeen-Roche K, Fried LP. Assistive devices in context: Cross-sectional association between challenges in the home environment and use of assistive devices for mobility. The Gerontologist. 2014; 54 (4):651–660. doi: 10.1093/geront/gnt030. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simbulan N. COVID-19 and its impact on higher education in the Philippines. Higher Education in Southeast Asia and beyond. 2020; 8 :15–18. [ Google Scholar ]
• Singh K, Srivastav S, Bhardwaj A, Dixit A, Misra S. Medical education during the COVID-19 pandemic: a single institution experience. Indian Pediatrics. 2020; 57 (7):678–679. doi: 10.1007/s13312-020-1899-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Singh V, Thurman A. How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018) American Journal of Distance Education. 2019; 33 (4):289–306. doi: 10.1080/08923647.2019.1663082. [ CrossRef ] [ Google Scholar ]
• Spector P. Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of Organizational Behavior. 1994; 15 (5):385–392. doi: 10.1002/job.4030150503. [ CrossRef ] [ Google Scholar ]
• Suryaman M, Cahyono Y, Muliansyah D, Bustani O, Suryani P, Fahlevi M, Munthe AP. COVID-19 pandemic and home online learning system: Does it affect the quality of pharmacy school learning? Systematic Reviews in Pharmacy. 2020; 11 :524–530. [ Google Scholar ]
• Tallent-Runnels MK, Thomas JA, Lan WY, Cooper S, Ahern TC, Shaw SM, Liu X. Teaching courses online: A review of the research. Review of Educational Research. 2006; 76 (1):93–135. doi: 10.3102/00346543076001093. [ CrossRef ] [ Google Scholar ]
• Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19.  Interactive Learning Environments , 1–12.
• Usher M, Barak M. Team diversity as a predictor of innovation in team projects of face-to-face and online learners. Computers & Education. 2020; 144 :103702. doi: 10.1016/j.compedu.2019.103702. [ CrossRef ] [ Google Scholar ]
• Varea, V., & González-Calvo, G. (2020). Touchless classes and absent bodies: Teaching physical education in times of Covid-19.  Sport, Education and Society , 1–15.
• Wallace RM. Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information. 2003; 3 (2):241–280. doi: 10.1080/14636310303143. [ CrossRef ] [ Google Scholar ]
• World Health Organization (2020). Coronavirus . https://www.who.int/health-topics/coronavirus#tab=tab_1
• Xue, E., Li, J., Li, T., & Shang, W. (2020). China’s education response to COVID-19: A perspective of policy analysis.  Educational Philosophy and Theory , 1–13.

Americans are embracing flexible work—and they want more of it

When the COVID-19 pandemic shuttered workplaces nationwide, society was plunged into an unplanned experiment in work from home. Nearly two-and-a-half years on, organizations worldwide have created new working norms  that acknowledge that flexible work is no longer a temporary pandemic response but an enduring feature of the modern working world.

About the survey

This article is based on a 25-minute, online-only Ipsos poll conducted on behalf of McKinsey between March 15 and April 18, 2022. A sample of 25,062 adults aged 18 and older from the continental United States, Alaska, and Hawaii was interviewed online in English and Spanish. To better reflect the population of the United States as a whole, post hoc weights were made to the population characteristics on gender, age, race/ethnicity, education, region, and metropolitan status. Given the limitations of online surveys, 1 “Internet surveys,” Pew Research Center. it is possible that biases were introduced because of undercoverage or nonresponse. People with lower incomes, less education, people living in rural areas, or people aged 65 and older are underrepresented among internet users and those with high-speed internet access.

The third edition of McKinsey’s American Opportunity Survey  provides us with data on how flexible work fits into the lives of a representative cross section of workers in the United States. McKinsey worked alongside the market-research firm Ipsos to query 25,000 Americans in spring 2022 (see sidebar, “About the survey”).

The most striking figure to emerge from this research is 58 percent. That’s the number of Americans who reported having the opportunity to work from home at least one day a week. 1 Many of the survey questions asked respondents about their ability or desire to “work from home.” “Work from home” is sometimes called “remote work,” while arrangements that allow for both remote and in-office work are often interchangeably labeled “hybrid” or “flexible” arrangements. We prefer the term flexible, which acknowledges that home is only one of the places where work can be accomplished and because it encompasses a variety of arrangements, whereas hybrid implies an even split between office and remote work. Thirty-five percent of respondents report having the option to work from home five days a week. What makes these numbers particularly notable is that respondents work in all kinds of jobs, in every part of the country and sector of the economy, including traditionally labeled “blue collar” jobs that might be expected to demand on-site labor as well as “white collar” professions.

About the authors

This article is a collaborative effort by André Dua , Kweilin Ellingrud , Phil Kirschner , Adrian Kwok, Ryan Luby, Rob Palter , and Sarah Pemberton as part of ongoing McKinsey research to understand the perceptions of and barriers to economic opportunity in America. The following represents the perspectives of McKinsey’s Real Estate and People & Organizational Performance Practices.

Another of the survey’s revelations: when people have the chance to work flexibly, 87 percent of them take it. This dynamic is widespread across demographics, occupations, and geographies. The flexible working world was born of a frenzied reaction to a sudden crisis but has remained as a desirable job feature for millions. This represents a tectonic shift in where, when, and how Americans want to work and are working.

The following six charts examine the following:

• the number of people offered flexible working arrangements either part- or full-time
• how many days a week employed people are offered and do work from home
• the gender, age, ethnicity, education level, and income of people working or desiring to work flexibly
• which occupations have the greatest number of remote workers and how many days a week they work remotely
• how highly employees rank flexible working arrangements as a reason to seek a new job
• impediments to working effectively for people who work remotely all the time, part of the time, or not at all

Flexible work’s implications for employees and employers—as well as for real estate, transit, and technology, to name a few sectors—are vast and nuanced and demand contemplation.

1. Thirty-five percent of job holders can work from home full-time, and 23 percent can do so part-time

A remarkable 58 percent of employed respondents—which, extrapolated from the representative sample, is equivalent to 92 million people from a cross section of jobs and employment types—report having the option to work from home for all or part of the week. After more than two years of observing remote work and predicting that flexible working would endure  after the acute phases of the COVID-19 pandemic, we view these data as a confirmation that there has been a major shift in the working world and in society itself.

We did not ask about flexible work in our American Opportunity Survey in past years, but an array of other studies indicate that flexible working has grown by anywhere from a third to tenfold since 2019. 1 Rachel Minkin et al., “How the coronavirus outbreak has—and hasn’t—changed the way Americans work,” Pew Research Center, December 9, 2020; “Telework during the COVID-19 pandemic: Estimates using the 2021 Business Response Survey,” US Bureau of Labor Statistics, Monthly Labor Review, March 2022.

Thirty-five percent of respondents say they can work from home full-time. Another 23 percent can work from home from one to four days a week. A mere 13 percent of employed respondents say they could work remotely at least some of the time but opt not to.

Forty-one percent of employed respondents don’t have the choice. This may be because not all work can be done remotely  or because employers simply demand on-site work. Given workers’ desire for flexibility, employers may have to explore ways to offer the flexibility employees want  to compete for talent effectively.

2. When offered, almost everyone takes the opportunity to work flexibly

The results of the survey showed that not only is flexible work popular, with 80 million Americans engaging in it (when the survey results are extrapolated to the wider population), but many want to work remotely for much of the week when given the choice.

Eighty-seven percent of workers offered at least some remote work embrace the opportunity and spend an average of three days a week working from home. People offered full-time flexible work spent a bit more time working remotely, on average, at 3.3 days a week. Interestingly, 12 percent of respondents whose employers only offer part-time or occasional remote work say that even they worked from home for five days a week. This contradiction appears indicative of a tension between how much flexibility employers offer and what employees demand .

3. Most employees want flexibility, but the averages hide the critical differences

There’s remarkable consistency among people of different genders, ethnicities, ages, and educational and income levels: the vast majority of those who can work from home do so. In fact, they just want more flexibility: although 58 percent of employed respondents say they can work from home at least part of the time, 65 percent of employed respondents say they would be willing to do so all the time.

However, the opportunity is not uniform: there was a large difference in the number of employed men who say they were offered remote-working opportunities (61 percent) and women (52 percent). At every income level, younger workers were more likely than older workers to report having work-from-home opportunities.

People who could but don’t work flexibly tend to be older (19 percent of 55- to 64-year-olds offered remote work didn’t take it, compared with 12 to 13 percent of younger workers) or have lower incomes (17 percent of those earning $25,000 to $74,999 per year who were offered remote work didn’t take it, compared with 10 percent of those earning over $75,000 a year). While some workers may choose to work on-site because they prefer the environment, others may feel compelled to because their home environments are not suitable, because they lack the skills and tools to work remotely productively, or because they believe there is an advantage to being on-site. Employers should be aware that different groups perceive and experience remote work differently and consider how flexible working fits with their diversity, equity, and inclusion strategies .

4. Most industries support some flexibility, but digital innovators demand it

The opportunity to work flexibly differs by industry and role within industries and has implications for companies competing for talent. For example, the vast majority of employed people in computer and mathematical occupations report having remote-work options, and 77 percent report being willing to work fully remotely. Because of rapid digital transformations across industries , even those with lower overall work-from-home patterns may find that the technologists they employ demand it.

A surprisingly broad array of professions offer remote-work arrangements. Half of respondents working in educational instruction and library occupations and 45 percent of healthcare practitioners and workers in technical occupations say they do some remote work, perhaps reflecting the rise of online education and telemedicine. Even food preparation and transportation professionals said they do some work from home.

5. Job seekers highly value having autonomy over where and when they work

The survey asked people if they had hunted for a job recently or were planning to hunt for one. Unsurprisingly, the most common rationale for a job hunt was a desire for greater pay or more hours, followed by a search for better career opportunities. The third-most-popular reason was looking for a flexible working arrangement.

Prior McKinsey research has shown that for those that left the workforce during the early phases of the COVID-19 pandemic, workplace flexibility was a top reason that they accepted new jobs . Employers should be aware that when a candidate is deciding between job offers with similar compensation, the opportunity to work flexibly can become the deciding factor.

6. Employees working flexibly report obstacles to peak performance

The survey asked respondents to identify what made it hard to perform their jobs effectively. Those working in a flexible model were most likely to report multiple obstacles, followed by those working fully remotely, and then by those working in the office. Our research doesn’t illuminate the cause and effect here: it could be that people who face barriers are more likely to spend some time working from home. It could also be that workers who experience both on-site and at-home work are exposed to the challenges of each and the costs of regularly switching contexts.

Some obstacles were reported at much higher rates by specific groups: for example, about 55 percent of 18- to 34-year-olds offered the option to work fully remotely say mental-health issues  impacted their ability to perform effectively, though only 17 percent of people aged 55 to 64 said the same. Workers with children at home  who were offered full-time remote-work options were far more likely than their peers without children to report that problems with physical health or a hostile work environment had a moderate or major impact on their job.

The results of the American Opportunity Survey reflect sweeping changes in the US workforce, including the equivalent of 92 million workers offered flexible work, 80 million workers engaged in flexible work, and a large number of respondents citing a search for flexible work as a major motivator to find a new job.

Competition for top performers and digital innovators demands that employers understand how much flexibility their talent pool is accustomed to and expects. Employers are wise to invest in technology, adapt policies, and train employees to create workplaces that integrate people working remotely and on-site (without overcompensating by requiring that workers spend too much time in video meetings ). The survey results identify obstacles to optimal performance that underscore a need for employers to support workers with issues that interfere with effective work. Companies will want to be thoughtful about which roles can be done partly or fully remotely—and be open to the idea that there could be more of these than is immediately apparent. Employers can define the right metrics and track them to make sure the new flexible model is working.

At a more macro level, a world in which millions of people no longer routinely commute has meaningful implications for the commercial core in big urban centers and for commercial real estate overall. Likewise, such a world implies a different calculus for where Americans will live and what types of homes they will occupy. As technology emerges that eliminates the residual barriers to more distributed and asynchronous work, it could become possible to move more types of jobs overseas, with potentially significant consequences.

In time, the full impact of flexible working will be revealed. Meanwhile, these data give us early insight into how the working world is evolving.

For more on the imperative for flexible work and how organizations can respond, please see McKinsey.com/featured-insights/ Future-of-the-workplace .

André Dua is a senior partner in McKinsey’s Miami office;  Kweilin Ellingrud is a senior partner in the Minneapolis office;  Phil Kirschner is a senior expert in the New York office, where Adrian Kwok is an associate partner and Ryan Luby is a senior expert; Rob Palter is a senior partner in the Toronto office; and Sarah Pemberton is a manager in the Hong Kong office.

Explore a career with us

Related articles.

Hybrid work: Making it fit with your diversity, equity, and inclusion strategy

Workplace real estate in the COVID-19 era: From cost center to competitive advantage