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Systematic review article, a systematic review of the effectiveness of online learning in higher education during the covid-19 pandemic period.

• 1 Department of Basic Education, Beihai Campus, Guilin University of Electronic Technology Beihai, Beihai, Guangxi, China
• 2 School of Sports and Arts, Harbin Sport University, Harbin, Heilongjiang, China
• 3 School of Music, Harbin Normal University, Harbin, Heilongjiang, China
• 4 School of General Education, Beihai Vocational College, Beihai, Guangxi, China
• 5 School of Economics and Management, Beihai Campus, Guilin University of Electronic Technology, Guilin, Guangxi, China

Background: The effectiveness of online learning in higher education during the COVID-19 pandemic period is a debated topic but a systematic review on this topic is absent.

Methods: The present study implemented a systematic review of 25 selected articles to comprehensively evaluate online learning effectiveness during the pandemic period and identify factors that influence such effectiveness.

Results: It was concluded that past studies failed to achieve a consensus over online learning effectiveness and research results are largely by how learning effectiveness was assessed, e.g., self-reported online learning effectiveness, longitudinal comparison, and RCT. Meanwhile, a set of factors that positively or negatively influence the effectiveness of online learning were identified, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience.

Discussion: Although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education and these challenges and difficulties are more prominent in developing countries. In addition, this review critically assesses limitations in past research, develops pedagogical implications, and proposes recommendations for future research.

1 Introduction

1.1 research background.

The COVID-19 pandemic first out broken in early 2020 has considerably shaped the higher education landscape globally. To restrain viral transmission, universities globally locked down, and teaching and learning activities were transferred to online platforms. Although online learning is a relatively mature learning model and is increasingly integrated into higher education, the sudden and unprepared transition to wholly online learning caused by the pandemic posed formidable challenges to higher education stakeholders, e.g., policymakers, instructors, and students, especially at the early stage of the pandemic ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Correspondingly, the effectiveness of online learning during the pandemic period is still questionable as online learning during this period has some unique characteristics, e.g., the lack of preparation, sudden and unprepared transition, the huge scale of implementation, and social distancing policies ( Sharma et al., 2020 ; Rahman, 2021 ; Tsang et al., 2021 ; Hollister et al., 2022 ; Zhang and Chen, 2023 ). This question is more prominent in developing or undeveloped countries because of insufficient Internet access, network problems, the lack of electronic devices, and poor network infrastructure ( Adnan and Anwar, 2020 ; Muthuprasad et al., 2021 ; Rahman, 2021 ; Chandrasiri and Weerakoon, 2022 ).

Learning effectiveness is a key consideration of education as it reflects the extent to which learning and teaching objectives are achieved and learners’ needs are satisfied ( Joy and Garcia, 2000 ; Swan, 2003 ). Online learning was generally proven to be effective within a higher education context ( Kebritchi et al., 2017 ) prior to the pandemic. ICTs have fundamentally shaped the process of learning as they allow learners to learn anywhere and anytime, interact with others efficiently and conveniently, and freely acquire a large volume of learning materials online ( Kebritchi et al., 2017 ; Choudhury and Pattnaik, 2020 ). Such benefits may be offset by the challenges brought about by the pandemic. A lot of empirical studies globally have investigated the effectiveness of online learning but there is currently a scarcity of a systematic review of these studies to comprehensively evaluate online learning effectiveness and identify factors that influence effectiveness.

At present, although the vast majority of countries have implemented opening policies to deal with the pandemic and higher education institutes have recovered offline teaching and learning, assessing the effectiveness of online learning during the pandemic period via a systematic review is still essential. First, it is necessary to summarize, learn from, and reflect on the lessons and experiences of online learning practices during the pandemic period to offer implications for future practices and research. Second, the review of online learning research carried out during the pandemic period is likely to generate interesting knowledge because of the unique research context. Third, higher education institutes still need a contingency plan for emergency online learning to deal with potential crises in the future, e.g., wars, pandemics, and natural disasters. A systematic review of research on the effectiveness of online learning during the pandemic period offers valuable knowledge for designing a contingency plan for the future.

1.2 Related concepts

1.2.1 online learning.

Online learning should not be simply understood as learning on the Internet or the integration of ICTs with learning because it is a systematic framework consisting of a set of pedagogies, technologies, implementations, and processes ( Kebritchi et al., 2017 ; Choudhury and Pattnaik, 2020). Choudhury and Pattnaik (2020; p.2) summarized prior definitions of online learning and provided a comprehensive and up-to-date definition, i.e., online learning refers to “ the transfer of knowledge and skills, in a well-designed course content that has established accreditations, through an electronic media like the Internet, Web 4.0, intranets and extranets .” Online learning differs from traditional learning because of not only technological differences, but also differences in social development and pedagogies ( Camargo et al., 2020 ). Online learning has also considerably shaped the patterns by which knowledge is stored, shared, and transferred, skills are practiced, as well as the way by which stakeholders (e.g., teachers and teachers) interact ( Desai et al., 2008 ; Anderson and Hajhashemi, 2013 ). In addition, online learning has altered educational objectives and learning requirements. Memorizing knowledge was traditionally viewed as vital to learning but it is now less important since required knowledge can be conveniently searched and acquired on the Internet while the reflection and application of knowledge becomes more important ( Gamage et al., 2023 ). Online learning also entails learners’ self-regulated learning ability more than traditional learning because the online learning environment inflicts less external regulation and provides more autonomy and flexibility ( Barnard-Brak et al., 2010 ; Wong et al., 2019 ). The above differences imply that traditional pedagogies may not apply to online learning.

There are a variety of online learning models according to the differences in learning methods, processes, outcomes, and the application of technologies ( Zeitoun, 2008 ). As ICTs can be used as either the foundation of learning or auxiliary means, online learning can be classified into assistant, blended, and wholly online models. Here, assistant online learning refers to the scenario where online learning technologies are used to supplement and support traditional learning; blended online learning refers to the integration/ mixture of online and offline methods, and; wholly online learning refers to the exclusive use of the Internet for learning ( Arkorful and Abaidoo, 2015 ). The present review focuses on wholly online learning because the review is interested in the COVID-19 pandemic context where learning activities are fully switched to online platforms.

1.2.2 Learning effectiveness

Learning effectiveness can be broadly defined as the extent to which learning and teaching objectives have been effectively and efficiently achieved via educational activities ( Swan, 2003 ) or the extent to which learners’ needs are satisfied by learning activities ( Joy and Garcia, 2000 ). It is a multi-dimensional construct because learning objectives and needs are always diversified ( Joy and Garcia, 2000 ; Swan, 2003 ). Assessing learning effectiveness is a key challenge in educational research and researchers generally use a set of subjective and objective indicators to assess learning effectiveness, e.g., examination scores, assignment performance, perceived effectiveness, student satisfaction, learning motivation, engagement in learning, and learning experience ( Rajaram and Collins, 2013 ; Noesgaard and Ørngreen, 2015 ). Prior research related to the effectiveness of online learning was diversified in terms of learning outcomes, e.g., satisfaction, perceived effectiveness, motivation, and learning engagement, and there is no consensus over which outcomes are valid indicators of learning effectiveness. The present study adopts a broad definition of learning effectiveness and considers various learning outcomes that are closely associated with learning objectives and needs.

1.3 Previous review research

Up to now, online learning during the COVID-19 pandemic period has attracted considerable attention from academia and there is a lot of related review research. Some review research analyzed the trends and major topics in related research. Pratama et al. (2020) tracked the trend of using online meeting applications in online learning during the pandemic period based on a systematic review of 12 articles. It was reported that the use of these applications kept a rising trend and this use helps promote learning and teaching processes. However, this review was descriptive and failed to identify problems related to these applications as well as the limitations of these applications. Zhang et al. (2022) implemented a bibliometric review to provide a holistic view of research on online learning in higher education during the COVID-19 pandemic period. They concluded that the majority of research focused on identifying the use of strategies and technologies, psychological impacts brought by the pandemic, and student perceptions. Meanwhile, collaborative learning, hands-on learning, discovery learning, and inquiry-based learning were the most frequently discussed instructional approaches. In addition, chemical and medical education were found to be the most investigated disciplines. This review hence offered a relatively comprehensive landscape of related research in the field. However, since it was a bibliometric review, it merely analyzed the superficial characteristics of past articles in the field without a detailed analysis of their research contributions. Bughrara et al. (2023) categorized the major research topics in the field of online medical education during the pandemic period via a scoping review. A total of 174 articles were included in the review and it was found there were seven major topics, including students’ mental health, stigma, student vaccination, use of telehealth, students’ physical health, online modifications and educational adaptations, and students’ attitudes and knowledge. Overall, the review comprehensively reveals major topics in the focused field.

Some scholars believed that online learning during the pandemic period has brought about a lot of problems while both students and teachers encounter many challenges. García-Morales et al. (2021) implemented a systematic review to identify the challenges encountered by higher education in an online learning scenario during the pandemic period. A total of seven studies were included and it was found that higher education suddenly transferred to online learning and a lot of technologies and platforms were used to support online learning. However, this transition was hasty and forced by the extreme situation. Thus, various stakeholders in learning and teaching (e.g., students, universities, and teachers) encountered difficulties in adapting to this sudden change. To deal with these challenges, universities need to utilize the potential of technologies, improve learning experience, and meet students’ expectations. The major limitation of García-Morales et al. (2021) review of the small-sized sample. Meanwhile, García-Morales et al. (2021) also failed to systematically categorize various types of challenges. Stojan et al. (2022) investigated the changes to medical education brought about by the shift to online learning in the COVID-19 pandemic context as well as the lessons and impacts of these changes via a systematic review. A total of 56 articles were included in the analysis, it was reported that small groups and didactics were the most prevalent instructional methods. Although learning engagement was always interactive, teachers majorly integrated technologies to amplify and replace, rather than transform learning. Based on this, they argued that the use of asynchronous and synchronous formats promoted online learning engagement and offered self-directed and flexible learning. The major limitation of this review is that the article is somewhat descriptive and lacks the crucial evaluation of problems of online learning.

Review research has also focused on the changes and impacts brought by online learning during the pandemic period. Camargo et al. (2020) implemented a meta-analysis on seven empirical studies regarding online learning methods during the pandemic period to evaluate feasible online learning platforms, effective online learning models, and the optimal duration of online lectures, as well as the perceptions of teachers and students in the online learning process. Overall, it was concluded that the shift from offline to online learning is feasible, and; effective online learning needs a well-trained and integrated team to identify students’ and teachers’ needs, timely respond, and support them via digital tools. In addition, the pandemic has brought more or less difficulties to online learning. An obvious limitation of this review is the overly small-sized sample ( N  = 7), which offers very limited information, but the review tries to answer too many questions (four questions). Grafton-Clarke et al. (2022) investigated the innovation/adaptations implemented, their impacts, and the reasons for their selections in the shift to online learning in medical education during the pandemic period via a systematic review of 55 articles. The major adaptations implemented include the rapid shift to the virtual space, pre-recorded videos or live streaming of surgical procedures, remote adaptations for clinical visits, and multidisciplinary ward rounds and team meetings. Major challenges encountered by students and teachers include the need for technical resources, faculty time, and devices, the shortage of standardized telemedicine curricula, and the lack of personal interactions. Based on this, they criticized the quality of online medical education. Tang (2023) explored the impact of the pandemic on primary, secondary, and tertiary education in the pandemic context via a systematic review of 41 articles. It was reported that the majority of these impacts are negative, e.g., learning loss among learners, assessment and experiential learning in the virtual environment, limitations in instructions, technology-related constraints, the lack of learning materials and resources, and deteriorated psychosocial well-being. These negative impacts are amplified by the unequal distribution of resources, unfair socioeconomic status, ethnicity, gender, physical conditions, and learning ability. Overall, this review comprehensively criticizes the problems brought about by online learning during the pandemic period.

Very little review research evaluated students’ responses to online learning during the pandemic period. For instance, Salas-Pilco et al. (2022) evaluated the engagement in online learning in Latin American higher education during the COVID-19 pandemic period via a systematic review of 23 studies. They considered three dimensions of engagement, including affective, cognitive, and behavioral engagement. They described the characteristics of learning engagement and proposed suggestions for enhancing engagement, including improving Internet connectivity, providing professional training, transforming higher education, ensuring quality, and offering emotional support. A key limitation of the review is that these authors focused on describing the characteristics of engagement without identifying factors that influence engagement.

A synthesis of previous review research offers some implications. First, although learning effectiveness is an important consideration in educational research, review research is scarce on this topic and hence there is a lack of comprehensive knowledge regarding the extent to which online learning is effective during the COVID-19 pandemic period. Second, according to past review research that summarized the major topics of related research, e.g., Bughrara et al. (2023) and Zhang et al. (2022) , the effectiveness of online learning is not a major topic in prior empirical research and hence the author of this article argues that this topic has not received due attention from researchers. Third, some review research has identified a lot of problems in online learning during the pandemic period, e.g., García-Morales et al. (2021) and Stojan et al. (2022) . Many of these problems are caused by the sudden and rapid shift to online learning as well as the unique context of the pandemic. These problems may undermine the effectiveness of online learning. However, the extent to which these problems influence online learning effectiveness is still under-investigated.

1.4 Purpose of the review research

The research is carried out based on a systematic review of past empirical research to answer the following two research questions:

Q1: To what extent online learning in higher education is effective during the COVID-19 pandemic period?

Q2: What factors shape the effectiveness of online learning in higher education during the COVID-19 pandemic period?

2 Research methodology

2.1 literature review as a research methodology.

Regardless of discipline, all academic research activities should be related to and based on existing knowledge. As a result, scholars must identify related research on the topic of interest, critically assess the quality and content of existing research, and synthesize available results ( Linnenluecke et al., 2020 ). However, this task is increasingly challenging for scholars because of the exponential growth of academic knowledge, which makes it difficult to be at the forefront and keep up with state-of-the-art research ( Snyder, 2019 ). Correspondingly, literature review, as a research methodology is more relevant than previously ( Snyder, 2019 ; Linnenluecke et al., 2020 ). A well-implemented review provides a solid foundation for facilitating theory development and advancing knowledge ( Webster and Watson, 2002 ). Here, a literature review is broadly defined as a more or less systematic way of collecting and synthesizing past studies ( Tranfield et al., 2003 ). It allows researchers to integrate perspectives and results from a lot of past research and is able to address research questions unanswered by a single study ( Snyder, 2019 ).

There are generally three types of literature review, including meta-analysis, bibliometric review, and systematic review ( Snyder, 2019 ). A meta-analysis refers to a statistical technique for integrating results from a large volume of empirical research (majorly quantitative research) to compare, identify, and evaluate patterns, relationships, agreements, and disagreements generated by research on the same topic ( Davis et al., 2014 ). This study does not adopt a meta-analysis for two reasons. First, the research on the effectiveness of online learning in the context of the COVID-19 pandemic was published since 2020 and currently, there is a limited volume of empirical evidence. If the study adopts a meta-analysis, the sample size will be small, resulting in limited statistical power. Second, as mentioned above, there are a variety of indicators, e.g., motivation, satisfaction, experience, test score, and perceived effectiveness ( Rajaram and Collins, 2013 ; Noesgaard and Ørngreen, 2015 ), that reflect different aspects of online learning effectiveness. The use of diversified effectiveness indicators increases the difficulty of carrying out meta-analysis.

A bibliometric review refers to the analysis of a large volume of empirical research in terms of publication characteristics (e.g., year, journal, and citation), theories, methods, research questions, countries, and authors ( Donthu et al., 2021 ) and it is useful in tracing the trend, distribution, relationship, and general patterns of research published in a focused topic ( Wallin, 2005 ). A bibliometric review does not fit the present study for two reasons. First, at present, there are less than 4 years of history of research on online learning effectiveness. Hence the volume of relevant research is limited and the public trend is currently unclear. Second, this study is interested in the inner content and results of articles published, rather than their external characteristics.

A systematic review is a method and process of critically identifying and appraising research in a specific field based on predefined inclusion and exclusion criteria to test a hypothesis, answer a research question, evaluate problems in past research, identify research gaps, and/or point out the avenue for future research ( Liberati et al., 2009 ; Moher et al., 2009 ). This type of review is particularly suitable to the present study as there are still a lot of unanswered questions regarding the effectiveness of online learning in the pandemic context, a need for indicating future research direction, a lack of summary of relevant research in this field, and a scarcity of critical appraisal of problems in past research.

Adopting a systematic review methodology brings multiple benefits to the present study. First, it is helpful for distinguishing what needs to be done from what has been done, identifying major contributions made by past research, finding out gaps in past research, avoiding fruitless research, and providing insights for future research in the focused field ( Linnenluecke et al., 2020 ). Second, it is also beneficial for finding out new research directions, needs for theory development, and potential solutions for limitations in past research ( Snyder, 2019 ). Third, this methodology helps scholars to efficiently gain an overview of valuable research results and theories generated by past research, which inspires their research design, ideas, and perspectives ( Callahan, 2014 ).

Commonly, a systematic review can be either author-centric or theme-centric ( Webster and Watson, 2002 ) and the present review is theme-centric. Specifically, an author-centric review focuses on works published by a certain author or a group of authors and summarizes the major contributions made by the author(s; ( Webster and Watson, 2002 ). This type of review is problematic in terms of its incompleteness of research conclusions in a specific field and descriptive nature ( Linnenluecke et al., 2020 ). A theme-centric review is more common where a researcher guides readers through reviewing themes, concepts, and interesting phenomena according to a certain logic ( Callahan, 2014 ). A theme in this review can be further structured into several related sub-themes and this type of review helps researchers to gain a comprehensive understanding of relevant academic knowledge ( Papaioannou et al., 2016 ).

2.2 Research procedures

This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline ( Liberati et al., 2009 ) to implement a systematic review. The guideline indicates four phases of performing a systematic review, including (1) identifying possible research, (2) abstract screening, (3) assessing full-text for eligibility, and (4) qualitatively synthesizing included research. Figure 1 provides a flowchart of the process and the number of articles excluded and included in each phase.

Figure 1 . PRISMA flowchart concerning the selection of articles.

This study uses multiple academic databases to identify possible research, e.g., Academic Search Complete, IGI Global, ACM Digital Library, Elsevier (SCOPUS), Emerald, IEEE Xplore, Web of Science, Science Direct, ProQuest, Wiley Online Library, Taylor and Francis, and EBSCO. Since the COVID-19 pandemic broke out in January 2020, this study limits the literature search to articles published from January 2020 to August 2023. During this period, online learning was highly prevalent in schools globally and a considerable volume of articles were published to investigate various aspects of online learning in this period. Keywords used for searching possible research include pandemic, COVID, SARS-CoV-2, 2019-nCoV, coronavirus, online learning, e-learning, electronic learning, higher education, tertiary education, universities, learning effectiveness, learning satisfaction, learning engagement, and learning motivation. Aside from searching from databases, this study also manually checks the reference lists of relevant articles and uses Google Scholar to find out other articles that have cited these articles.

2.3 Inclusion and exclusion criteria

Articles included in the review must meet the following criteria. First, articles have to be written in English and published on peer-reviewed journals. The academic language being English was chosen because it is in the Q zone of the specified search engines. Second, the research must be carried out in an online learning context. Third, the research must have collected and analyzed empirical data. Fourth, the research should be implemented in a higher education context and during the pandemic period. Fifth, the outcome variable must be factors related to learning effectiveness, and included studies must have reported the quantitative results for online learning effectiveness. The outcome variable should be measured by data collected from students, rather than other individuals (e.g., instructors). For instance, the research of Rahayu and Wirza (2020) used teacher perception as a measurement of online learning effectiveness and was hence excluded from the sample. According to the above criteria, a total of 25 articles were included in the review.

2.4 Data extraction and analysis

Content analysis is performed on included articles and an inductive approach is used to answer the two research questions. First, to understand the basic characteristics of the 25 articles/studies, the researcher summarizes their types, research designs, and samples and categorizes them into several groups. The researcher carefully reads the full-text of these articles and codes valuable pieces of content. In this process, an inductive approach is used, and key themes in these studies have been extracted and summarized. Second, the researcher further categorizes these studies into different groups according to their similarities and differences in research findings. In this way, these studies are broadly categorized into three groups, i.e., (1) ineffective (2) neutral, and (3) effective. Based on this, the research answers the research question and indicates the percentage of studies that evidenced online learning as effective in a COVID-19 pandemic context. The researcher also discusses how online learning is effective by analyzing the learning outcomes brought by online learning. Third, the researcher analyzes and compares the characteristics of the three groups of studies and extracts key themes that are relevant to the conditional effectiveness of online learning from these studies. Based on this, the researcher identifies factors that influence the effectiveness of online learning in a pandemic context. In this way, the two research questions have been adequately answered.

3 Research results and discussion

3.1 study characteristics.

Table 1 shows the statistics of the 25 studies while Table 2 shows a summary of these studies. Overall, these studies varied greatly in terms of research design, research subjects, contexts, measurements of learning effectiveness, and eventually research findings. Approximately half of the studies were published in 2021 and the number of studies reduced in 2022 and 2023, which may be attributed to the fact that universities gradually implemented opening-up policies after 2020. China received the largest number of studies ( N  = 5), followed by India ( N = 4) and the United States ( N  = 3). The sample sizes of the majority of studies (88.0%) ranged between 101 and 500. As this review excluded qualitative studies, all studies included adopted a purely quantitative design (88.0%) or a mixed design (12.0%). The majority of the studies were cross-sectional (72%) and a few studies (2%) were experimental.

Table 1 . Statistics of studies included in the review.

Table 2 . A summary of studies reviewed.

3.2 The effectiveness of online learning

Overall, the 25 studies generated mixed results regarding the effectiveness of online learning during the pandemic period. 9 (36%) studies reported online learning as effective; 13 (52%) studies reported online learning as ineffective, and the rest 3 (12%) studies produced neutral results. However, it should be noted that the results generated by these studies are not comparable as they used different approaches to evaluate the effectiveness of online learning. According to the approach of evaluating online learning effectiveness, these studies are categorized into four groups, including (1) Cross-sectional evaluation of online learning effectiveness without a comparison with offline learning; without a control group ( N  = 14; 56%), (2) Cross-sectional comparison of the effectiveness of online learning with offline learning; without control group (7; 28%), (3) Longitudinal comparison of the effectiveness of online learning with offline learning, without a control group ( N  = 2; 8%), and (4) Randomized Controlled Trial (RCT); with a control group ( N  = 2; 8%).

The first group of studies asked students to report the extent to which they perceived online learning as effective, they had achieved expected learning outcomes through online learning, or they were satisfied with online learning experience or outcomes, without a comparison with offline learning. Six out of 14 studies reported online learning as ineffective, including Adnan and Anwar (2020) , Hong et al. (2021) , Mok et al. (2021) , Baber (2022) , Chandrasiri and Weerakoon (2022) , and Lalduhawma et al. (2022) . Five out of 14 studies reported online learning as effective, including Almusharraf and Khahro (2020) , Sharma et al. (2020) , Mahyoob (2021) , Rahman (2021) , and Haningsih and Rohmi (2022) . In addition, 3 out of 14 studies reported neutral results, including Cranfield et al. (2021) , Tsang et al. (2021) , and Conrad et al. (2022) . It should be noted that this measurement approach is problematic in three aspects. First, researchers used various survey instruments to measure learning effectiveness without reaching a consensus over a widely accepted instrument. As a result, these studies measured different aspects of learning effectiveness and hence their results may be incomparable. Second, these studies relied on students’ self-reports to evaluate learning effectiveness, which is subjective and inaccurate. Third, even though students perceived online learning as effective, it does not imply that online learning is more effective than offline learning because of the absence of comparables.

The second group of studies asked students to compare online learning with offline learning to evaluate learning effectiveness. Interestingly, all 7 studies, including Alawamleh et al. (2020) , Almahasees et al. (2021) , Gonzalez-Ramirez et al. (2021) , Muthuprasad et al. (2021) , Selco and Habbak (2021) , Hollister et al. (2022) , and Zhang and Chen (2023) , reported that online learning was perceived by participants as less effective than offline learning. It should be noted that these results were specific to the COVID-19 pandemic context where strict social distancing policies were implemented. Consequently, these results should be interpreted as online learning during the school lockdown period was perceived by participants as less effective than offline learning during the pre-pandemic period. A key problem of the measurement of learning effectiveness in these studies is subjectivity, i.e., students’ self-reported online learning effectiveness relative to offline learning may be subjective and influenced by a lot of factors caused by the pandemic, e.g., negative emotions (e.g., fear, loneliness, and anxiety).

Only two studies implemented a longitudinal comparison of the effectiveness of online learning with offline learning, i.e., Chang et al. (2021) and Fyllos et al. (2021) . Interestingly, both studies reported that participants perceived online learning as more effective than offline learning, which is contradicted with the second group of studies. In the two studies, the same group of students participated in offline learning and online learning successively and rated the effectiveness of the two learning approaches, respectively. The two studies were implemented by time coincidence, i.e., researchers unexpectedly encountered the pandemic and subsequently, school lockdown when they were investigating learning effectiveness. Such time coincidence enabled them to compare the effectiveness of offline and online learning. However, this research design has three key problems. First, the content of learning in the online and offline learning periods was different and hence the evaluations of learning effectiveness of the two periods are not comparable. Second, self-reported learning effectiveness is subjective. Third, students are likely to obtain better examination scores in online examinations than in offline examinations because online examinations bring a lot of cheating behaviors and are less fair than offline examinations. As reported by Fyllos et al. (2021) , the examination score after online learning was significantly higher than after offline learning. Chang et al. (2021) reported that participants generally believed that offline examinations are fairer than online examinations.

Lastly, only two studies, i.e., Jiang et al. (2023) and Shirahmadi et al. (2023) , implemented an RCT design, which is more persuasive, objective, and accurate than the above-reviewed studies. Indeed, implementing an RCT to evaluate the effectiveness of online learning was a formidable challenge during the pandemic period because of viral transmission and social distancing policies. Both studies reported that online learning is more effective than offline learning during the pandemic period. However, it is questionable about the extent to which such results are affected by health/safety-related issues. It is reasonable to infer that online learning was perceived by students as safer than offline learning during the pandemic period and such perceptions may affect learning effectiveness.

Overall, it is difficult to conclude whether online learning is effective during the pandemic period. Nevertheless, it is possible to identify factors that shape the effectiveness of online learning, which is discussed in the next section.

3.3 Factors that shape online learning effectiveness

Infrastructure factors were reported as the most salient factors that determine online learning effectiveness. It seems that research from developed countries generated more positive results for online learning than research from less developed countries. This view was confirmed by the cross-country comparative study of Cranfield et al. (2021) . Indeed, online learning entails the support of ICT infrastructure, and hence ICT related factors, e.g., Internet connectivity, technical issues, network speed, accessibility of digital devices, and digital devices, considerably influence the effectiveness of online learning ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Prior review research, e.g., Tang (2023) also suggested that the unequal distribution of resources and unfair socioeconomic status intensified the problems brought about by online learning during the pandemic period. Salas-Pilco et al. (2022) recommended that improving Internet connectivity would increase students’ engagement in online learning during the pandemic period.

Adnan and Anwar (2020) study is one of the most cited works in the focused field. They reported that online learning is ineffective in Pakistan because of the problems of Internet access due to monetary and technical issues. The above problems hinder students from implementing online learning activities, making online learning ineffective. Likewise, Lalduhawma et al. (2022) research from India indicated that online learning is ineffective because of poor network interactivity, slow data speed, low data limits, and expensive costs of devices. As a result, online learning during the COVID-19 pandemic may have expanded the education gap between developed and developing countries because of developing countries’ infrastructure disadvantages. More attention to online learning infrastructure problems in developing countries is needed.

Instructional factors, e.g., course management and design, instructor characteristics, instructor-student interaction, assignments, and assessments were found to affect online learning effectiveness ( Sharma et al., 2020 ; Rahman, 2021 ; Tsang et al., 2021 ; Hollister et al., 2022 ; Zhang and Chen, 2023 ). Although these instructional factors have been well-documented as significant drivers of learning effectiveness in traditional learning literature, these factors in the pandemic period have some unique characteristics. Both students and teachers were not well prepared for wholly online instruction and learning in 2020 and hence they encountered a lot of problems in course management and design, learning interactions, assignments, and assessments ( Stojan et al., 2022 ; Tang, 2023 ). García-Morales et al. (2021) review also suggested that various stakeholders in learning and teaching encountered difficulties in adapting to the sudden, hasty, and forced transition of offline to online learning. Consequently, these instructional factors become salient in terms of affecting online learning effectiveness.

The negative role of the lack of social interaction caused by social distancing in affecting online learning effectiveness was highlighted by a lot of studies ( Almahasees et al., 2021 ; Baber, 2022 ; Conrad et al., 2022 ; Hollister et al., 2022 ). Baber (2022) argued that people give more importance to saving lives than socializing in the online environment and hence social interactions in learning are considerably reduced by social distancing norms. The negative impact of the lack of social interaction on online learning effectiveness is reflected in two aspects. First, according to a constructivist view, interaction is an indispensable element of learning because knowledge is actively constructed by learners in social interactions ( Woo and Reeves, 2007 ). Consequently, online learning effectiveness during the pandemic period is reduced by the lack of social interaction. Second, the lack of social interaction brings a lot of negative emotions, e.g., feelings of isolation, loneliness, anxiety, and depression ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). Such negative emotions undermine online learning effectiveness.

Negative emotions caused by the pandemic and school lockdown were also found to be detrimental to online learning effectiveness. In this context, it was reported that many students experience a lot of negative emotions, e.g., feelings of isolation, exhaustion, loneliness, and distraction ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). Such negative emotions, as mentioned above, reduce online learning effectiveness.

Several factors were also found to increase online learning effectiveness during the pandemic period, e.g., convenience and flexibility ( Hong et al., 2021 ; Muthuprasad et al., 2021 ; Selco and Habbak, 2021 ). Students with strong self-regulated learning abilities gain more benefits from convenience and flexibility in online learning ( Hong et al., 2021 ).

Overall, although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education. Meanwhile, the majority of students prefer offline learning to online learning. The above challenges and difficulties are more prominent in developing countries than in developed countries.

3.4 Pedagogical implications

The results generated by the systematic review offer a lot of pedagogical implications. First, online learning entails the support of ICT infrastructure, and infrastructure defects strongly undermine learning effectiveness ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Given the fact online learning is increasingly integrated into higher education ( Kebritchi et al., 2017 ) regardless of the presence of the pandemic, governments globally should increase the investment in learning-related ICT infrastructure in higher education institutes. Meanwhile, schools should consider students’ affordability of digital devices and network fees when implementing online learning activities. It is important to offer material support for those students with poor economic status. Infrastructure issues are more prominent in developing countries because of the lack of monetary resources and poor infrastructure base. Thus, international collaboration and aid are recommended to address these issues.

Second, since the lack of social interaction is a key factor that reduces online learning effectiveness, it is important to increase social interactions during the implementation of online learning activities. On the one hand, both students and instructors are encouraged to utilize network technologies to promote inter-individual interactions. On the other hand, the two parties are also encouraged to engage in offline interaction activities if the risk is acceptable.

Third, special attention should be paid to students’ emotions during the online learning process as online learning may bring a lot of negative emotions to students, which undermine learning effectiveness ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). In addition, higher education institutes should prepare a contingency plan for emergency online learning to deal with potential crises in the future, e.g., wars, pandemics, and natural disasters.

3.5 Limitations and suggestions for future research

There are several limitations in past research regarding online learning effectiveness during the pandemic period. The first is the lack of rigor in assessing learning effectiveness. Evidently, there is a scarcity of empirical research with an RCT design, which is considered to be accurate, objective, and rigorous in assessing pedagogical models ( Torgerson and Torgerson, 2001 ). The scarcity of ICT research leads to the difficulty in accurately assessing the effectiveness of online learning and comparing it with offline learning. Second, the widely accepted criteria for assessing learning effectiveness are absent, and past empirical studies used diversified procedures, techniques, instruments, and criteria for measuring online learning effectiveness, resulting in difficulty in comparing research results. Third, learning effectiveness is a multi-dimensional construct but its multidimensionality was largely ignored by past research. Therefore, it is difficult to evaluate which dimensions of learning effectiveness are promoted or undermined by online learning and it is also difficult to compare the results of different studies. Finally, there is very limited knowledge about the difference in online learning effectiveness between different subjects. It is likely that the subjects that depend on lab-based work (e.g., experimental physics, organic chemistry, and cell biology) are less appropriate for online learning than the subjects that depend on desk-based work (e.g., economics, psychology, and literature).

To deal with the above limitations, there are several recommendations for future research on online learning effectiveness. First, future research is encouraged to adopt an RCT design and collect a large-sized sample to objectively, rigorously, and accurately quantify the effectiveness of online learning. Second, scholars are also encouraged to develop a new framework to assess learning effectiveness comprehensively. This framework should cover multiple dimensions of learning effectiveness and have strong generalizability. Finally, it is recommended that future research could compare the effectiveness of online learning between different subjects.

4 Conclusion

This study carried out a systematic review of 25 empirical studies published between 2020 and 2023 to evaluate the effectiveness of online learning during the COVID-19 pandemic period. According to how online learning effectiveness was assessed, these 25 studies were categorized into four groups. The first group of studies employed a cross-sectional design and assessed online learning based on students’ perceptions without a control group. Less than half of these studies reported online learning as effective. The second group of studies also employed a cross-sectional design and asked students to compare the effectiveness of online learning with offline learning. All these studies reported that online learning is less effective than offline learning. The third group of studies employed a longitudinal design and compared the effectiveness of online learning with offline learning but without a control group and this group includes only 2 studies. It was reported that online learning is more effective than offline learning. The fourth group of studies employed an RCT design and this group includes only 2 studies. Both studies reported online learning as more effective than offline learning.

Overall, it is difficult to conclude whether online learning is effective during the pandemic period because of the diversified research contexts, methods, and approaches in past research. Nevertheless, the review identifies a set of factors that positively or negatively influence the effectiveness of online learning, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience. Although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education. Meanwhile, the majority of students prefer offline learning to online learning. In addition, developing countries face more challenges and difficulties in online learning because of monetary and infrastructure issues.

The findings of this review offer significant pedagogical implications for online learning in higher education institutes, including enhancing the development of ICT infrastructure, providing material support for students with poor economic status, enhancing social interactions, paying attention to students’ emotional status, and preparing a contingency plan of emergency online learning.

The review also identifies several limitations in past research regarding online learning effectiveness during the pandemic period, including the lack of rigor in assessing learning effectiveness, the absence of accepted criteria for assessing learning effectiveness, the neglect of the multidimensionality of learning effectiveness, and limited knowledge about the difference in online learning effectiveness between different subjects.

To deal with the above limitations, there are several recommendations for future research on online learning effectiveness. First, future research is encouraged to adopt an RCT design and collect a large-sized sample to objectively, rigorously, and accurately quantify the effectiveness of online learning. Second, scholars are also encouraged to develop a new framework to assess learning effectiveness comprehensively. This framework should cover multiple dimensions of learning effectiveness and have strong generalizability. Finally, it is recommended that future research could compare the effectiveness of online learning between different subjects. To fix these limitations in future research, recommendations are made.

It should be noted that this review is not free of problems. First, only studies that quantitatively measured online learning effectiveness were included in the review and hence a lot of other studies (e.g., qualitative studies) that investigated factors that influence online learning effectiveness were excluded, resulting in a relatively small-sized sample and incomplete synthesis of past research contributions. Second, since this review was qualitative, it was difficult to accurately quantify the level of online learning effectiveness.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

WM: Writing – original draft, Writing – review & editing. LY: Writing – original draft, Writing – review & editing. CL: Writing – review & editing. NP: Writing – review & editing. XP: Writing – review & editing. YZ: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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.

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Keywords: COVID-19 pandemic, higher education, online learning, learning effectiveness, systematic review

Citation: Meng W, Yu L, Liu C, Pan N, Pang X and Zhu Y (2024) A systematic review of the effectiveness of online learning in higher education during the COVID-19 pandemic period. Front. Educ . 8:1334153. doi: 10.3389/feduc.2023.1334153

Received: 06 November 2023; Accepted: 27 December 2023; Published: 17 January 2024.

Reviewed by:

Copyright © 2024 Meng, Yu, Liu, Pan, Pang and Zhu. 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: Lei Yu, [email protected]

How Effective Is Online Learning? What the Research Does and Doesn’t Tell Us

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Editor’s Note: This is part of a series on the practical takeaways from research.

The times have dictated school closings and the rapid expansion of online education. Can online lessons replace in-school time?

Clearly online time cannot provide many of the informal social interactions students have at school, but how will online courses do in terms of moving student learning forward? Research to date gives us some clues and also points us to what we could be doing to support students who are most likely to struggle in the online setting.

The use of virtual courses among K-12 students has grown rapidly in recent years. Florida, for example, requires all high school students to take at least one online course. Online learning can take a number of different forms. Often people think of Massive Open Online Courses, or MOOCs, where thousands of students watch a video online and fill out questionnaires or take exams based on those lectures.

In the online setting, students may have more distractions and less oversight, which can reduce their motivation.

Most online courses, however, particularly those serving K-12 students, have a format much more similar to in-person courses. The teacher helps to run virtual discussion among the students, assigns homework, and follows up with individual students. Sometimes these courses are synchronous (teachers and students all meet at the same time) and sometimes they are asynchronous (non-concurrent). In both cases, the teacher is supposed to provide opportunities for students to engage thoughtfully with subject matter, and students, in most cases, are required to interact with each other virtually.

Coronavirus and Schools

Online courses provide opportunities for students. Students in a school that doesn’t offer statistics classes may be able to learn statistics with virtual lessons. If students fail algebra, they may be able to catch up during evenings or summer using online classes, and not disrupt their math trajectory at school. So, almost certainly, online classes sometimes benefit students.

In comparisons of online and in-person classes, however, online classes aren’t as effective as in-person classes for most students. Only a little research has assessed the effects of online lessons for elementary and high school students, and even less has used the “gold standard” method of comparing the results for students assigned randomly to online or in-person courses. Jessica Heppen and colleagues at the American Institutes for Research and the University of Chicago Consortium on School Research randomly assigned students who had failed second semester Algebra I to either face-to-face or online credit recovery courses over the summer. Students’ credit-recovery success rates and algebra test scores were lower in the online setting. Students assigned to the online option also rated their class as more difficult than did their peers assigned to the face-to-face option.

Most of the research on online courses for K-12 students has used large-scale administrative data, looking at otherwise similar students in the two settings. One of these studies, by June Ahn of New York University and Andrew McEachin of the RAND Corp., examined Ohio charter schools; I did another with colleagues looking at Florida public school coursework. Both studies found evidence that online coursetaking was less effective.

About this series

This essay is the fifth in a series that aims to put the pieces of research together so that education decisionmakers can evaluate which policies and practices to implement.

The conveners of this project—Susanna Loeb, the director of Brown University’s Annenberg Institute for School Reform, and Harvard education professor Heather Hill—have received grant support from the Annenberg Institute for this series.

To suggest other topics for this series or join in the conversation, use #EdResearchtoPractice on Twitter.

Read the full series here .

It is not surprising that in-person courses are, on average, more effective. Being in person with teachers and other students creates social pressures and benefits that can help motivate students to engage. Some students do as well in online courses as in in-person courses, some may actually do better, but, on average, students do worse in the online setting, and this is particularly true for students with weaker academic backgrounds.

Students who struggle in in-person classes are likely to struggle even more online. While the research on virtual schools in K-12 education doesn’t address these differences directly, a study of college students that I worked on with Stanford colleagues found very little difference in learning for high-performing students in the online and in-person settings. On the other hand, lower performing students performed meaningfully worse in online courses than in in-person courses.

But just because students who struggle in in-person classes are even more likely to struggle online doesn’t mean that’s inevitable. Online teachers will need to consider the needs of less-engaged students and work to engage them. Online courses might be made to work for these students on average, even if they have not in the past.

Just like in brick-and-mortar classrooms, online courses need a strong curriculum and strong pedagogical practices. Teachers need to understand what students know and what they don’t know, as well as how to help them learn new material. What is different in the online setting is that students may have more distractions and less oversight, which can reduce their motivation. The teacher will need to set norms for engagement—such as requiring students to regularly ask questions and respond to their peers—that are different than the norms in the in-person setting.

Online courses are generally not as effective as in-person classes, but they are certainly better than no classes. A substantial research base developed by Karl Alexander at Johns Hopkins University and many others shows that students, especially students with fewer resources at home, learn less when they are not in school. Right now, virtual courses are allowing students to access lessons and exercises and interact with teachers in ways that would have been impossible if an epidemic had closed schools even a decade or two earlier. So we may be skeptical of online learning, but it is also time to embrace and improve it.

A version of this article appeared in the April 01, 2020 edition of Education Week as How Effective Is Online Learning?

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The problem with online learning? It doesn’t teach people to think

Professor, Department of Communication Arts, University of Waterloo

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Robert Danisch receives funding from the Social Sciences and Humanities Research Council of Canada.

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The modern research university was designed to produce new knowledge and to pass that knowledge on to students. North American universities over the last 100 years have been exceptionally good at that task.

But this is not all that universities can do or should do. The COVID-19 pandemic has made it even easier to reduce teaching to knowledge dissemination and to obscure other, equally important, forms of education that help students be better citizens, thinkers, writers and collaborators.

These other forms of education are the cornerstone of human flourishing and democratic participation.

This is a problem.

Practical wisdom

The Ancient Greeks relied on a distinction between “knowing-that” ( episteme ) and “knowing-how” ( techne ) . This was the difference between an abstract body of theoretical knowledge about an area of interest and the practical wisdom necessary to carry out a specific task.

In music, for instance, we might call this the difference between knowing what pitch means, what notes are or the other aspects of music theory that help explain how to play — and knowing how to play an instrument like the piano really well.

For American philosopher John Dewey , this amounts to the difference between an education that focuses on information and an education that focuses on habits of thinking and deliberation.

In How We Think and Democracy and Education , Dewey prioritized teaching how to solve problems over bodies of knowledge because he knew that improved thinking skills would produce better outcomes for students and for public life.

Dewey believed that acquiring knowing-how habits, like critical thinking, problem-solving and close reading, required interaction and imitation. The practices of reading, speaking and thinking were all intertwined for Dewey, and all required practice and reflection. Practising these related skills would improve our decision-making, as individuals and as communities.

The kind of imitation he had in mind — people imitating each other — is impossible in a remote setting.

Dewey also thought curiosity, along with a recognition of, and confrontation with, real problems set people in the direction of improved thinking. These were modelled by teachers through engagement and interaction with students.

How We Think also argues that teaching students habits of using language for the purposes of persuasion is a central part of education. This drew Dewey’s work quite close to classical conceptions of rhetoric, or the teaching of how to speak and write effectively (including the emphasis on imitation as central to mastering the techne of communication).

These commitments were necessarily embodied in live practice in the classroom.

Know-how compromised online

The modern research university, since the late 19th century, has tended to prioritize “knowing-that” over “knowing-how” in a wide range of different disciplines (despite Dewey’s attempt to articulate an alternative).

Urban studies and planning professor Donald Schon’s work at the Massachusetts Institute of Technology on reflective practice was an attempt to correct this over-emphasis and apply Dewey’s approach to contemporary curricula. But the emphasis on “knowing-that” persists.

Remote learning is well suited to the kinds of education that focus on abstract theoretical knowledge and not “know-how.” And this is exactly the problem with those forms of learning — and why we ought to resist being seduced by them.

Some researchers argue that the adequacy of online learning is demonstrated by the fact that a cohort of students might achieve the same grades in an online setting as in an in-person setting. This justifies the assumption that there is no significant difference in academic performance between the two settings.

But my analysis of how people learn, grounded in rhetorical studies and Dewey’s emphasis on embodied and practical forms of democratic education, and also in my own experience administering a first-year seminar program in a faculty of arts, points to the fact that it is much harder to teach (and to assess) the “knowing-how” skills that will matter more to students’ future success.

These include learning outcomes like knowing how to analyze data, collaboration with peers, self-reflection and reading and writing.

Drowning in specialized knowledge

Specialized bodies of knowledge are everywhere now , not just in lecture halls or within the ivy-covered walls of elite institutions. If you want knowledge about advanced python programming or mycology, you can find it online through a range of different media for free. This is why silicon valley gurus can question the value of a degree from an expensive university .

The threat to the university is this: boundless “knowing-that” is readily and easily available to any student because of the very same media that have made the transition to remote teaching easy. But the same is not true for the lived experience required for developing “knowing-how” habits and practices.

As we drown in ever-increasing amounts of available knowledge, our “knowing-how” forms of wisdom continue to suffer. This is true for elementary school students that need school to learn how to navigate social relationships and for university students trying to learn how to use the scientific method or perform a critical, close reading of a poem.

Careful and close readings

To teach a student how to carefully read a text, for example, is a responsibility of the university. But this feels unlikely in remote learning environments. Dewey’s focus on the importance of the interaction between student and teacher, the modelling and imitation of habits of thinking and the necessity of creative and collaborative problem solving in the classroom are all made more difficult in a remote setting.

An isolated 18-year-old, staring at a computer, can learn what a text is supposed to mean but will have a much harder time learning how to perform a careful interpretation.

It is also one of the many “knowing-how” skills that seem so broadly absent in our public culture. Close reading is akin to close listening, which is a requirement of collaboration and a precursor to self-reflection. Journalist Kate Murphy’s You’re Not Listening shows just how complex the embodied task of reading someone else can be and how important listening and reading are for success in all fields.

What we ought to ask

Instead of asking how universities might benefit from shifting courses and curricula online permanently, we ought to be asking how students might suffer from fewer opportunities to focus on “knowing-how” and ever-greater commitments to “knowing-that.”

The pandemic has shown that we need finer, more well-honed and well-practised “knowing-how” skills. Skills like: asking thoughtful questions, finding new evidence, testing hypotheses, collaborating with diverse others , critically evaluating data or evidence, performing analysis of source material and designing new methods of evaluation.

These forms of wrestling and questioning are largely lost online. They get easily replaced with rote information processing. We should worry about the outcomes associated with that shift.

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• Published: 09 May 2024

Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors

• Yujie Su   ORCID: orcid.org/0000-0003-1444-1598 1 ,
• Xiaoshu Xu   ORCID: orcid.org/0000-0002-0667-4511 1 ,
• Yunfeng Zhang 2 ,
• Xinyu Xu 1 &
• Shanshan Hao 3  

Humanities and Social Sciences Communications volume  11 , Article number:  594 ( 2024 ) Cite this article

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This study delves into the retrospections of undergraduate students concerning their online learning experiences after the COVID-19 pandemic, using the nine key influencing factors: behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation. 46 Year 1 students from a comprehensive university in China were asked to maintain reflective diaries throughout an academic semester, providing first-person perspectives on the strengths and weaknesses of online learning. Meanwhile, 18 college teachers were interviewed with the same questions as the students. Using thematic analysis, the research identified 9 factors. The research revealed that instruction ranked highest among the 9 factors, followed by engagement, self-regulation, interaction, motivation, and others. Moreover, teachers and students had different attitudes toward instruction. Thirdly, teacher participants were different from student participants given self-efficacy and self-regulation due to their variant roles in online instruction. Lastly, the study reflected students were not independent learners, which explained why instruction ranked highest in their point of view. Findings offer valuable insights for educators, administrators, and policy-makers involved in higher education. Recommendations for future research include incorporating a more diverse sample, exploring relationships between the nine factors, and focusing on equipping students with skills for optimal online learning experiences.

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Introduction.

The outbreak of the COVID-19 pandemic has had a profound impact on education worldwide, leading to the widescale adoption of online learning. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), at the peak of the pandemic, 192 countries had implemented nationwide closures, affecting approximately 99% of the world’s student population (UNESCO 2020 a). In response, educational institutions, teachers, and students quickly adapted to online learning platforms, leveraging digital technologies to continue education amidst the crisis (Marinoni et al. 2020 ).

The rapid and unexpected shift to online learning brought about a surge in research aiming to understand its impact, effectiveness, and challenges. Researchers across the globe have been investigating various dimensions of online learning. Some focus on students’ experiences and perspectives (Aristovnik et al. 2021 ), technological aspects (Bao 2020 ), pedagogical strategies (Hodges et al. 2020 ), and the socio-emotional aspect of learning (Ali 2020 ). Tan et al. ( 2021 ) found that motivation and satisfaction were mostly positively perceived by students, and lack of interaction was perceived as an unfavorable online instruction perception. Some center on teachers’ perceptions of the benefits and challenges (Lucas and Vicente, 2023 ; Mulla et al. 2023 ), post-pandemic pedagogisation (Rapanta et al. 2021 ), and post-pandemic further education (Kohnke et al. 2023 ; Torsani et al. 2023 ). It was worth noting that elements like interaction and engagement were central to the development and maintenance of the learning community (Lucas and Vincente 2023 ),

The rise of online learning has also posed unprecedented challenges. Studies have pointed out the digital divide and accessibility issues (Crawford et al. 2020 ), students’ motivation and engagement concerns (Martin and Bolliger 2018 ), and the need for effective online instructional practices (Trust and Whalen 2020 ). The rapid transition to online learning has highlighted the need for robust research to address these challenges and understand the effectiveness of online learning in this new educational paradigm.

Despite the extensive research on online learning during and after the COVID-19 pandemic, there remains a notable gap in understanding the retrospective perspectives of both undergraduates and teachers. Much of the current literature has focused on immediate response strategies to the transition to online learning, often overlooking the detailed insights that reflective retrospection can provide (Marinoni et al. 2020 ; Bao 2020 ). In addition, while many studies have examined isolated aspects of online learning, they have not often employed a comprehensive framework, leaving undergraduates’ voices, in particular, underrepresented in the discourse (Aristovnik et al. 2021 ; Crawford et al. 2020 ). This study, situated in the context of the COVID-19 pandemic’s impetus toward online learning, seeks to fill this crucial gap. By exploring online learning from the perspectives of both instructors and undergraduates, and analyzing nine key factors that include engagement, motivation, and self-efficacy, the research contributes vital insights into the dynamics of online education (Wang and Wang 2021 ). This exploration is especially pertinent as digital learning environments become increasingly prevalent worldwide (UNESCO 2020b ). The findings of our study are pivotal for shaping future educational policies and enhancing online education strategies in this continuously evolving educational landscape (Greenhow et al. 2021 ). Thus, three research questions were raised:

Q1: How do undergraduates and teachers in China retrospectively perceive the effectiveness of online learning after the COVID-19 pandemic?

Q2: Which of the nine outcome influencing factors had the most significant impact on online learning experiences after the pandemic, and why?

Q3: What recommendations can be proposed to enhance the effectiveness of online learning in the future?

The research takes place at a comprehensive university in China, with a sample of 46 Year 1 students and 18 experienced teachers. Their reflections on the effectiveness of online learning were captured through reflective diaries guided by four questions. These questions investigated the students’ online learning states and attitudes, identified issues and insufficiencies in online learning, analyzed the reasons behind these problems, and proposed improvements. By assessing their experiences and perceptions, we seek to explore the significant factors that shaped online learning outcomes after the pandemic and the means to enhance its effectiveness.

This paper first presents a review of the existing literature, focusing on the impact of the pandemic on online learning and discussing the nine significant factors influencing online learning outcomes. Following this, the methodology utilized for this study is detailed, setting the stage for a deeper understanding of the research process. Subsequently, we delve into the results of the thematic analysis conducted based on undergraduate students and teachers’ retrospections. Finally, the paper concludes by offering meaningful implications of the findings for various stakeholders and suggesting directions for future research in this critical area.

Literature review

Online learning application and evaluation in higher education.

Online learning, also known as e-learning or distance learning, refers to education that takes place over the Internet rather than in a traditional classroom setting. It has seen substantial growth over the past decade and has been accelerated due to the COVID-19 pandemic (Trust and Whalen 2020 ). Online learning allows for a flexible learning environment, breaking the temporal and spatial boundaries of traditional classroom settings (Bozkurt and Sharma 2020 ). In response to the COVID-19 pandemic, educational institutions globally have embraced online learning at an unprecedented scale. This has led to an immense surge in research focusing on the effects of the pandemic on online learning (Crawford et al. 2020 ; Marinoni et al. 2020 ).

Researchers were divided in their attitudes toward the effects of online learning, including positive, neutral, and negative. Research by Bahasoan et al. ( 2020 ), Bernard et al. ( 2004 ), Hernández-Lara and Serradell-López ( 2018 ), and Paechter and Maier ( 2010 ) indicated the effectiveness of online learning, including improved outcomes and engagement in online formats, providing flexibility and enhancing digital skills for instance. Research, including studies by Dolan Hancock and Wareing ( 2015 ) and Means et al. ( 2010 ), indicates that under equivalent conditions and with similar levels of support, there is frequently no substantial difference in learning outcomes between traditional face-to-face courses and completely online courses.

However, online learning was not without its challenges. Research showing less favorable results for specific student groups can be referenced in Dennen ( 2014 ), etc. The common problems faced by students included underdeveloped independent learning ability, lack of motivation, difficulties in self-regulation, student engagement and technical issues (Aristovnik et al. 2021 ; Martin and Bolliger 2018 ; Song et al. 2004 ; Zheng et al. 2022 ).

Moreover, factors like instructional strategies, course design, etc. were also linked to learning outcomes and successful online learning (Ali 2020 ; Hongsuchon et al. 2022 ). Careaga-Butter et al. ( 2020 ) critically analyze online education in pandemic and post-pandemic contexts, focusing on digital tools and resources for teaching in synchronous and asynchronous learning modalities. They discuss the swift adaptation to online learning during the pandemic, highlighting the importance of technological infrastructure, pedagogical strategies, and the challenges of digital divides. The article emphasizes the need for effective online learning environments and explores trends in post-pandemic education, providing insights into future educational strategies and practices.

Determinants of online learning outcomes

Online learning outcomes in this paper refer to the measurable educational results achieved through online learning methods, including knowledge acquisition, skill development, changes in attitudes or behaviors, and performance improvements (Chang 2016 ; Panigrahi et al. 2018 ). The literature review identified key factors influencing online learning outcomes, emphasizing their significant role in academic discourse. These factors, highlighted in scholarly literature, include student engagement, instructional design, technology infrastructure, student-teacher interaction, and student self-regulation.

Student Engagement: The level of a student’s engagement significantly impacts their learning outcomes. The more actively a student is engaged with the course content and activities, the better their performance tends to be. This underscores the importance of designing engaging course content and providing opportunities for active learning in an online environment (Martin and Bolliger 2018 ).

Instructional Design: How an online course is designed can greatly affect student outcomes. Key elements such as clarity of learning objectives, organization of course materials, and the use of diverse instructional strategies significantly impact student learning (Bozkurt and Sharma 2020 ).

Technology Infrastructure: The reliability and ease of use of the learning management system (LMS) also play a significant role in online learning outcomes. When students experience technical difficulties, it can lead to frustration, reduced engagement, and lower performance (Johnson et al. 2020 ).

Student-Teacher Interaction: Interaction between students and teachers in an online learning environment is a key determinant of successful outcomes. Regular, substantive feedback from instructors can promote student learning and motivation (Boling et al. 2012 ).

Student Self-Regulation: The autonomous nature of online learning requires students to be proficient in self-regulated learning, which involves setting learning goals, self-monitoring, and self-evaluation. Students who exhibit strong self-regulation skills are more likely to succeed in online learning (Broadbent 2017 ).

While many studies have investigated individual factors affecting online learning, there is a paucity of research offering a holistic view of these factors and their interrelationships, leading to a fragmented understanding of the influences on online learning outcomes. Given the multitude of experiences and variables encompassed by online learning, a comprehensive framework like is instrumental in ensuring a thorough investigation and interpretation of the breadth of students’ experiences.

Students’ perceptions of online learning

Understanding students’ perceptions of online learning is essential for enhancing its effectiveness and student satisfaction. Studies show students appreciate online learning for its flexibility and convenience, offering personalized learning paths and resource access (Händel et al. 2020 ; Johnson et al. 2020 ). Yet, challenges persist, notably in maintaining motivation and handling technical issues (Aristovnik et al. 2021 ; Händel et al. 2020 ). Aguilera-Hermida ( 2020 ) reported mixed feelings among students during the COVID-19 pandemic, including feelings of isolation and difficulty adjusting to online environments. Boling et al. ( 2012 ) emphasized students’ preferences for interactive and communicative online learning environments. Additionally, research indicates that students seek more engaging content and innovative teaching approaches, suggesting a gap between current online offerings and student expectations (Chakraborty and Muyia Nafukho 2014 ). Students also emphasize the importance of community and peer support in online settings, underlining the need for collaborative and social learning opportunities (Lai et al. 2019 ). These findings imply that while online learning offers significant benefits, addressing its shortcomings is critical for maximizing its potential.

The pandemic prompted a reconsideration of instructional modalities, with many students favoring face-to-face instruction due to the immediacy and focus issues (Aristovnik et al. 2021 ; Trust and Whalen 2020 ). Despite valuable insights, research gaps remain, particularly in long-term undergraduate reflections and the application of nine factors of comprehensive frameworks, indicating a need for more holistic research in online learning effectiveness.

Teachers’ perceptions of online learning

The pandemic has brought attention to how teachers manage instruction in virtual learning environments. Teachers and students are divided in terms of their attitudes toward online learning. Some teachers and students looked to the convenience and flexibility of online learning (Chuenyindee et al. 2022 ; Al-Emran and Shaalan 2021 ). They conceived that online learning provided opportunities to improve educational equality as well (Tenório et al. 2016 ). Even when COVID-19 was over, the dependence on online learning was likely here to stay, for some approaches of online learning were well-received by students and teachers (Al-Rahmi et al. 2019 ; Hongsuchon et al. 2022 ).

Teachers had shown great confidence in delivering instruction in an online environment in a satisfying manner. They also agreed that the difficulty of teaching was closely associated with course structures (Gavranović and Prodanović 2021 ).

Not all were optimistic about the effects of online learning. They sought out the challenges facing teachers and students during online learning.

A mixed-method study of K-12 teachers’ feelings, experiences, and perspectives that the major challenges faced by teachers during the COVID-19 pandemic were lack of student participation and engagement, technological support for online learning, lack of face-to-face interactions with students, no work-life balance and learning new technology.

The challenges to teachers’ online instruction included instruction technology (Maatuk et al. 2022 ; Rasheed et al. 2020 ), course design (Khojasteh et al. 2023 ), and teachers’ confidence (Gavranović and Prodanović 2021 ).

Self-regulation challenges and challenges in using technology were the key challenges to students, while the use of technology for teaching was the challenge facing teachers (Rasheed et al. 2020 ).

The quality of course design was another important factor in online learning. A research revealed the competency of the instructors and their expertise in content development contributed a lot to students’ satisfaction with the quality of e-contents.

Theoretical framework

The theoretical foundation of the research is deeply rooted in multifaceted framework for online learning, which provides a comprehensive and interwoven model encompassing nine critical factors that collectively shape the educational experience in online settings. This framework is instrumental in guiding our analysis and enhances the comparability and interpretability of our results within the context of existing literature.

Central to Yu’s framework is the concept of behavioral intention, which acts as a precursor to student engagement in online learning environments. This engagement, inherently linked to the students’ intentions and motivations, is significantly influenced by the quality of instruction they receive. Instruction, therefore, emerges as a pivotal element in this model, directly impacting not only student engagement but also fostering a sense of self-efficacy among learners. Such self-efficacy is crucial as it influences both the performance of students and their overall satisfaction with the learning process.

The framework posits that engagement, a derivative of both strong behavioral intention and effective instruction, plays a vital role in enhancing student performance. This engagement is tightly interlaced with self-regulation, an indispensable skill in the autonomous and often self-directed context of online learning. Interaction, encompassing various forms such as student-teacher and peer-to-peer communications, further enriches the learning experience. It significantly contributes to the development of motivation and self-efficacy, both of which are essential for sustaining engagement and fostering self-regulated learning.

Motivation, especially when intrinsically driven, acts as a catalyst, perpetuating engagement and self-regulation, which ultimately leads to increased satisfaction with the learning experience. In this framework, self-efficacy, nurtured through effective instruction and meaningful interactions, has a positive impact on students’ performance and satisfaction, thereby creating a reinforcing cycle of learning and achievement.

Performance in this model is viewed as a tangible measure of the synergistic interplay of engagement, instructional quality, and self-efficacy, while satisfaction reflects the culmination of the learning experience, shaped by the quality of instruction, the extent and nature of interactions, and the flexibility of the learning environment. This satisfaction, in turn, influences students’ future motivation and their continued engagement with online learning.

Yu’s model thus presents a dynamic ecosystem where changes in one factor can have ripple effects across the entire spectrum of online learning. It emphasizes the need for a holistic approach in the realm of online education, considering the complex interplay of these diverse yet interconnected elements to enhance both the effectiveness and the overall experience of online learning.

The current study employed a qualitative design to explore teachers’ and undergraduates’ retrospections on the effectiveness of online learning during the first semester of the 2022–2023 school year, which is in the post-pandemic period. Data were collected using reflective diaries, and thematic analysis was applied to understand the experiences based on the nine factors.

Sample and sampling

The study involved 18 teachers and 46 first-year students from a comprehensive university in China, selected through convenience sampling to ensure diverse representation across academic disciplines. To ensure a diverse range of experiences in online learning, the participant selection process involved an initial email inquiry about their prior engagement with online education. The first author of this study received ethics approval from the department research committee, and participants were informed of the study’s objectives two weeks before via email. Only those participants who provided written informed consent were included in the study and were free to withdraw at any time. Pseudonyms were used to protect participants’ identities during the data-coding process. For direct citations, acronyms of students’ names were used, while “T+number” was used for citations from teacher participants.

The 46 students are all first-year undergraduates, 9 females and 37 males majoring in English and non-English (see Table 1 ).

The 18 teachers are all experienced instructors with at least 5 years of teaching experience, 13 females and 5 male, majoring in English and Non-English (see Table 2 ).

Data collection

Students’ data were collected through reflective diaries in class during the first semester of the 2022–2023 school year. Each participant was asked to maintain a diary over the course of one academic semester, in which they responded to four questions.

The four questions include:

What was your state and attitude toward online learning?

What were the problems and shortcomings of online learning?

What do you think are the reasons for these problems?

What measures do you think should be taken to improve online learning?

This approach provided a first-person perspective on the participants’ online teaching or learning experiences, capturing the depth and complexity of their retrospections.

Teachers were interviewed separately by responding to the four questions the same as the students. Each interview was conducted in the office or the school canteen during the semester and lasted about 20 to 30 min.

Data analysis

We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. This method involved extensive engagement with the data, from initial coding to the final report. While Yu’s factors provided a foundational structure, we remained attentive to new themes, ensuring a comprehensive analysis. Our approach was methodical: familiarizing ourselves with the data, identifying initial codes, systematically searching and reviewing themes, and then defining and naming them. To validate our findings, we incorporated peer debriefing, and member checking, and maintained an audit trail. This analysis method was chosen for its effectiveness in extracting in-depth insights from undergraduates’ retrospections on their online learning experiences post-pandemic, aligning with our research objectives.

According to the nine factors, the interviews of 18 teachers and 46 Year 1 undergraduates were catalogued and listed in Table 3 .

Behavioral intention towards online learning post-pandemic

Since the widespread of the COVID-19 pandemic, both teachers and students have experienced online learning. However, their online teaching or learning was forced rather than planned (Baber 2021 ; Bao 2020 ). Students more easily accepted online learning when they perceived the severity of COVID-19.

When entering the post-pandemic era, traditional teaching was resumed. Students often compared online learning with traditional learning by mentioning learning interests, eye contact, face-to-face learning and learning atmosphere.

“I don’t think online learning is a good form of learning because it is hard to focus on learning.” (DSY) “In unimportant courses, I would let the computer log to the platform and at the same time do other entertains such as watching movies, listening to the music, having snacks or do the cleaning.” (XYN) “Online learning makes it impossible to have eye contact between teachers and students and unable to create a face-to-face instructional environment, which greatly influences students’ initiative and engagement in classes.” (WRX)

They noted that positive attitudes toward online learning usually generated higher behavioral intention to use online learning than those with negative attitudes, as found in the research of Zhu et al. ( 2023 ). So they put more blame on distractions in the learning environment.

“Online learning relies on computers or cell phones which easily brings many distractions. … I can’t focus on studying, shifting constantly from study and games.” (YX) “When we talk about learning online, we are hit by an idea that we can take a rest in class. It’s because everyone believes that during online classes, the teacher is unable to see or know what we are doing.” (YM) “…I am easily disturbed by external factors, and I am not very active in class.” (WZB)

Teachers reported a majority of students reluctantly turning on their cameras during online instruction and concluded the possible reason for such behavior.

“One of the reasons why some students are unwilling to turn on the camera is that they are worried about their looks and clothing at home, or that they don’t want to become the focus.” (T4)

They also noticed students’ absent-mindedness and lazy attitude during online instruction.

“As for some students who are not self-regulated, they would not take online learning as seriously as offline learning. Whenever they are logged onto the online platform, they would be unable to stay focused and keep their attention.” (T1)

Challenges and opportunities in online instruction post-pandemic

Online teaching brought new challenges and opportunities for students during and after the pandemic. The distractions at home seemed to be significantly underestimated by teachers in an online learning environment (Radmer and Goodchild 2021 ). It might be the reason why students greatly expected and heavily relied on teachers’ supervision and management.

“The biggest problem of online learning is that online courses are as imperative as traditional classes, but not managed face to face the same as the traditional ones.” (PC) “It is unable to provide some necessary supervision.” (GJX) “It is incapable of giving timely attention to every student.” (GYC) “Teachers can’t understand students’ conditions in time in most cases so teachers can’t adjust their teaching plan.” (MZY) “Some courses are unable to reach the teaching objectives due to lack of experimental conduction and practical operation.” (YZH) “Insufficient teacher-student interaction and the use of cell phones make both groups unable to engage in classes. What’s more, though online learning doesn’t put a high requirement for places, its instructional environment may be crucial due to the possible distractions.” (YCY)

Teachers also viewed online instruction as an addition to face-to-face instruction.

“Online learning cannot run as smoothly as face-to-face instruction, but it can provide an in-time supplement to the practical teaching and students’ self-learning.” (T13, T17) “Online instruction is an essential way to ensure the normal function of school work during the special periods like the pandemic” (T1, T15)

Factors influencing student engagement in online learning

Learning engagement was found to contribute to gains in the study (Paul and Diana 2006 ). It was also referred to as a state closely intertwined with the three dimensions of learning, i.e., vigor, dedication, and absorption (Schaufeli et al. 2002 ). Previous studies have found that some key factors like learning interaction, self-regulation, and social presence could influence learning engagement and learning outcomes (Lowenthal and Dunlap 2020 ; Ng 2018 ). Due to the absence of face-to-face interaction like eye contact, facial expressions and body language, both groups of interviewees agreed that the students felt it hard to keep their attention and thus remain active in online classes.

“Students are unable to engage in study due to a lack of practical learning environment of online learning.” (ZMH, T12) “Online platforms may not provide the same level of engagement and interaction as in-person classrooms, making it harder for students to ask questions or engage in discussions.” (HCK) “The Internet is cold, lack of emotional clues and practical connections, which makes it unable to reproduce face-to-face offline learning so that teachers and students are unlikely to know each other’s true feelings or thoughts. In addition, different from the real-time learning supervision in offline learning, online learning leaves students more learning autonomy.” (XGH) “Lack of teachers’ supervision and practical learning environment, students are easily distracted.” (LMA, T9)

Just as Zhu et al. ( 2023 ) pointed out, we had been too optimistic about students’ engagement in online learning, because online learning relied more on students’ autonomy and efforts to complete online learning.

Challenges in teacher-student interaction in online learning

Online learning has a notable feature, i.e., a spatial and temporal separation among teachers and students. Thus, online teacher-student interactions, fundamentals of relationship formation, have more challenges for both teachers and students. The prior studies found that online interaction affected social presence and indirectly affected learning engagement through social presence (Miao and Ma 2022 ). In the present investigation, both teachers and students noted the striking disadvantage of online interaction.

“Online learning has many problems such as indirect teacher-student communication, inactive informative communication, late response of students and their inability to reflect their problems. For example, teachers cannot evaluate correctly whether the students have mastered or not.” (YYN) “Teachers and students are separated by screens. The students cannot make prompt responses to the teachers’ questions via loudspeakers or headphones. It is not convenient for students to participate in questioning and answering. …for most of the time, the students interact with teachers via typing.” (ZJY) “While learning online, students prefer texting the questions to answering them via the loudspeaker.”(T7)

Online learning interaction was also found closely related to online learning engagement, performance, and self-efficacy.

“Teachers and students are unable to have timely and effective communication, which reduces the learning atmosphere. Students are often distracted. While doing homework, the students are unable to give feedback to teachers.” (YR) “Students are liable to be distracted by many other side matters so that they can keep their attention to online learning.” (T15)

In the online learning environment, teachers need to make efforts to build rapport and personalizing interactions with students to help them perform better and achieve greater academic success (Harper 2018 ; Ong and Quek 2023 ) Meanwhile, teachers should also motivate students’ learning by designing the lessons, giving lectures and managing the processes of student interactions (Garrison 2003 ; Ong and Quek 2023 ).

Determinants of self-efficacy in online learning

Online learning self-efficacy refers to students’ perception of their abilities to fulfill specific tasks required in online learning (Calaguas and Consunji 2022 ; Zimmerman and Kulikowich 2016 ). Online learning self-efficacy was found to be influenced by various factors including task, learner, course, and technology level, among which task level was found to be most closely related (Xu et al. 2022 ). The responses from the 46 student participants reveal a shared concern, albeit without mentioning specific tasks; they highlight critical aspects influencing online learning: learner attributes, course structure, and technological infrastructure.

One unifying theme from the student feedback is the challenge of self-regulation and environmental distractions impacting learning efficacy. For instance, participant WSX notes the necessity for students to enhance time management skills due to deficiencies in self-regulation, which is crucial for successful online learning. Participant WY expands on this by pointing out the distractions outside traditional classroom settings, coupled with limited teacher-student interaction, which hampers idea exchange and independent thought, thereby undermining educational outcomes. These insights suggest a need for strategies that bolster students’ self-discipline and interactive opportunities in virtual learning environments.

On the technological front, participants WT and YCY address different but related issues. Participant WT emphasizes the importance of up-to-date course content and learning facilities, indicating that outdated materials and tools can significantly diminish the effectiveness of online education. Participant YCY adds to this by highlighting problems with online learning applications, such as subpar functionalities that can introduce additional barriers to learning.

Teacher participants, on the other hand, shed light on objective factors predominantly related to course content and technology. Participant T5’s response underscores the heavy dependency on technological advancement in online education and points out the current inability of platforms or apps to adequately monitor student engagement and progress. Participant T9 voices concerns about course content not being updated or aligned with contemporary trends and student interests, suggesting a disconnect between educational offerings and learner needs. Meanwhile, participant T8 identifies unstable network services as a significant hindrance to online teaching, highlighting infrastructure as a critical component of online education’s success.

Teachers also believed the insufficient mastery of facilities and unfamiliarity with online instruction posed difficulty.

“Most teachers and students are not familiar with online instruction. For example, some teachers are unable to manage online courses so they cannot design the courses well. Some students lack self-regulation, which leads to their distraction or avoidance in class.” (T9)

Influences on student performance in online learning

Students’ performance during online lessons is closely associated with their satisfaction and self-efficacy. Most of the student participants reflected on their distractions, confusion, and needs, which indicates their dissatisfaction with online learning.

“During online instruction, it is convenient for the students to make use of cell phones, but instead, cell phones bring lots of distraction.” (YSC) “Due to the limits of online learning, teachers are facing the computer screen and unable to know timely students’ needs and confusion. Meanwhile, it’s inconvenient for teachers to make clear explanations of the sample questions or problems.” (HZW)

They thought their low learning efficiency in performance was caused by external factors like the learning environment.

“The most obvious disadvantage of online learning goes to low efficiency. Students find it hard to keep attention to study outside the practical classroom or in a relaxing environment.” (WY) “Teachers are not strict enough with students, which leads to ineffective learning.” (WRX)

Teacher participants conceived students’ performance as closely related to valid online supervision and students’ self-regulation.

“Online instruction is unable to create a learning environment, which helps teachers know students’ instant reaction. Only when students well regulate themselves and stay focused during online learning can they achieve successful interactions and make good accomplishments in the class.” (T11) “Some students need teachers’ supervision and high self-regulation, or they were easily distracted.” (T16)

Student satisfaction and teaching effectiveness in online learning

Online learning satisfaction was found to be significantly and positively associated with online learning self-efficacy (Al-Nasa’h et al. 2021 ; Lashley et al. 2022 ). Around 46% of student participants were unsatisfied with teachers’ ways of teaching.

“Comparatively, bloggers are more interesting than teachers’ boring and dull voices in online learning.” (DSY) “Teachers’ voice sounds dull and boring through the internet, which may cause listeners to feel sleepy, and the teaching content is not interesting enough to the students.” (MFE)

It reflected partly that some teachers were not adapted to online teaching possibly due to a lack in experience of online teaching or learning (Zhu et al. 2022 ).

“Some teachers are not well-prepared for online learning. They are particularly unready for emergent technological problems when delivering the teaching.” (T1) “One of the critical reasons lies in the fact that teachers and students are not well trained before online learning. In addition, the online platform is not unified by the college administration, which has led to chaos and difficulty of online instruction.” (T17)

Teachers recognized their inadequate preparation and mastery of online learning as one of the reasons for dissatisfaction, but student participants exaggerated the role of teachers in online learning and ignored their responsibility in planning and managing their learning behavior, as in the research of (Xu et al. 2022 ).

The role of self-regulation in online learning success

In the context of online learning, self-regulation stands out as a crucial factor, necessitating heightened levels of student self-discipline and autonomy. This aspect, as Zhu et al. ( 2023 ) suggest, grants students significant control over their learning processes, making it a vital component for successful online education.

“Online learning requires learners to be of high discipline and self-regulation. Without good self-regulation, they are less likely to be effective in online learning.” (YZJ) “Most students lack self-control, unable to control the time of using electronic products. Some even use other electronic products during online learning, which greatly reduces their efficiency in learning.” (GPY) “Students are not well developed in self-control and easily distracted. Thus they are unable to engage fully in their study, which makes them unable to keep up with others” (XYN)

Both groups of participants had a clear idea of the positive role of self-regulation in successful learning, but they also admitted that students need to strengthen their self-regulation skills and it seemed they associated with the learning environment, learning efficiency and teachers’ supervision.

“If they are self-motivated, online learning can be conducted more easily and more efficiently. However, a majority are not strong in regulating themselves. Teachers’ direct supervision in offline learning can do better in motivating them to study hard…lack of interaction makes students less active and motivated.” (LY) “Students have a low level of self-discipline. Without teachers’ supervision, they find it hard to listen attentively or even quit listening. Moreover, in class, the students seldom think actively and independently.” (T13)

The analysis of participant responses, categorized into three distinct attitude groups – positive, neutral, and negative – reveals a multifaceted view of the disadvantages of online learning, as shown in Tables 4 and 5 . This classification provides a clearer understanding of how attitudes towards online learning influence perceptions of self-regulation and other related factors.

In Table 4 , the division among students is most pronounced in terms of interaction and self-efficacy. Those with neutral attitudes highlighted interaction as a primary concern, suggesting that it is less effective in an online setting. Participants with positive attitudes noted a lack of student motivation, while those with negative views emphasized the need for better self-efficacy. Across all attitudes, instruction, engagement, self-regulation, and behavior intention were consistently identified as areas needing improvement.

Table 5 sheds light on teachers’ perspectives, revealing a consensus on the significance and challenges of instruction, motivation, and self-efficacy in online learning. Teachers’ opinions vary most significantly on self-efficacy and engagement. Those with negative attitudes point to self-efficacy and instructional quality as critical areas needing attention, while neutral attitudes focus on the role of motivation.

Discussions

Using a qualitative and quantitative analysis of the questionnaire data showed that among the 18 college teachers and 46 year 1 undergraduate students of various majors taking part in the interview, about 38.9% of teachers and about 30.4% of students supported online learning. Only two teachers were neutral about online learning, and 50% of teachers did not support virtual learning. The percentages of students who expressed positive and neutral views on online learning were the same, i.e., 34.8%. This indicates that online learning could serve as a complementary approach to traditional education, yet it is not without challenges, particularly in terms of student engagement, self-regulation, and behavioral intention, which were often attributed to distractions inherent in online environments.

In analyzing nine factors, it was evident that both teachers and students did not perceive these factors uniformly. Instruction was a significant element for both groups, as validated by findings in Tables 3 and 5 . The absence of face-to-face interactions in online learning shifted the focus to online instruction quality. Teachers cited technological challenges as a central concern, while students criticized the lack of engaging content and teaching methods. This aligns with Miao and Ma ( 2022 ), who argued that direct online interaction does not necessarily influence learner engagement, thus underscoring the need for integrated approaches encompassing interactions, self-regulation, and social presence.

Furthermore, the role of technology acceptance in shaping self-efficacy was highlighted by Xu et al. ( 2022 ), suggesting that students with higher self-efficacy tend to challenge themselves more. Chen and Hsu ( 2022 ) noted the positive influence of using emojis in online lessons, emphasizing the importance of innovative pedagogical approaches in online settings.

The study revealed distinct priorities between teachers and students in online learning: teachers emphasized effective instruction delivery, while students valued learning outcomes, self-regulation, and engagement. This divergence highlights the unique challenges each group faces. Findings by Dennen et al. ( 2007 ) corroborate this, showing instructors focusing on content and guidance, while students prioritize interpersonal communication and individualized attention. Additionally, Lee et al. ( 2011 ) found that reduced transactional distance and increased student engagement led to enhanced perceptions of learning outcomes, aligning with students’ priorities in online courses. Understanding these differing perspectives is crucial for developing comprehensive online learning strategies that address the needs of both educators and learners.

Integrating these findings with broader contextual elements such as technological infrastructure, pedagogical strategies, socio-economic backgrounds, and environmental factors (Balanskat and Bingimlas 2006 ) further enriches our understanding. The interplay between these external factors and Yu’s nine key aspects forms a complex educational ecosystem. For example, government interventions and training programs have been shown to increase teachers’ enthusiasm for ICT and its routine use in education (Balanskat and Bingimlas 2006 ). Additionally, socioeconomic factors significantly impact students’ experiences with online learning, as the digital divide in connectivity and access to computers at home influences the ICT experience, an important factor for school achievement (OECD 2015 ; Punie et al. 2006 ).

In sum, the study advocates for a holistic approach to understanding and enhancing online education, recognizing the complex interplay between internal factors and external elements that shape the educational ecosystem in the digital age.

Conclusion and future research

This study offered a comprehensive exploration into the retrospective perceptions of college teachers and undergraduate students regarding their experiences with online learning following the COVID-19 pandemic. It was guided by a framework encompassing nine key factors that influence online learning outcomes. To delve into these perspectives, the research focused on three pivotal questions. These questions aimed to uncover how both undergraduates and teachers in China view the effectiveness of online learning post-pandemic, identify which of the nine influencing factors had the most significant impact, and propose recommendations for enhancing the future effectiveness of online learning.

In addressing the first research question concerning the retrospective perceptions of online learning’s effectiveness among undergraduates and teachers in China post-COVID-19 pandemic, the thematic analysis has delineated clear divergences in attitude between the two demographics. Participants were primarily divided into three categories based on their stance toward online learning: positive, neutral, and negative. The results highlighted a pronounced variance in attitude distribution between teachers and students, with a higher percentage of teachers expressing clear-cut opinions, either favorably or unfavorably, towards the effectiveness of online learning.

Conversely, students displayed a pronounced inclination towards neutrality, revealing a more cautious or mixed stance on the effectiveness of online learning. This prevalent neutrality within the student body could be attributed to a range of underlying reasons. It might signify students’ uncertainties or varied experiences with online platforms, differences in engagement levels, gaps in digital literacy, or fluctuating quality of online materials and teaching methods. Moreover, this neutral attitude may arise from the psychological and social repercussions of the pandemic, which have potentially altered students’ approaches to and perceptions of learning in an online context.

In the exploration of the nine influential factors in online learning, it was discovered that both teachers and students overwhelmingly identified instruction as the most critical aspect. This was closely followed by engagement, interaction, motivation, and other factors, while performance and satisfaction were perceived as less influential by both groups. However, the attitudes of teachers and students towards these factors revealed notable differences, particularly about instruction. Teachers often attributed challenges in online instruction to technological issues, whereas students perceived the quality of instruction as a major influence on their learning effectiveness. This dichotomy highlights the distinct perspectives arising from their different roles within the educational process.

A further divergence was observed in views on self-efficacy and self-regulation. Teachers, with a focus on delivering content, emphasized the importance of self-efficacy, while students, grappling with the demands of online learning, prioritized self-regulation. This reflects their respective positions in the online learning environment, with teachers concerned about the efficacy of their instructional strategies and students about managing their learning process. Interestingly, the study also illuminated that students did not always perceive themselves as independent learners, which contributed to the high priority they placed on instruction quality. This insight underlines a significant area for development in online learning strategies, emphasizing the need for fostering greater learner autonomy.

Notably, both teachers and students concurred that stimulating interest was a key factor in enhancing online learning. They proposed innovative approaches such as emulating popular online personalities, enhancing interactive elements, and contextualizing content to make it more relatable to students’ lives. Additionally, practical suggestions like issuing preview tasks and conducting in-class quizzes were highlighted as methods to boost student engagement and learning efficiency. The consensus on the importance of supervisory roles underscores the necessity for a balanced approach that integrates guidance and independence in the online learning environment.

The outcomes of our study highlight the multifaceted nature of online learning, accentuated by the varied perspectives and distinct needs of teachers and students. This complexity underscores the necessity of recognizing and addressing these nuances when designing and implementing online learning strategies. Furthermore, our findings offer a comprehensive overview of both the strengths and weaknesses of online learning during an unprecedented time, offering valuable insights for educators, administrators, and policy-makers involved in higher education. Moreover, it emphasized the intricate interplay of multiple factors—behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation—in shaping online learning outcomes. presents some limitations, notably its reliance on a single research method and a limited sample size.

However, the exclusive use of reflective diaries and interviews restricts the range of data collection methods, which might have been enriched by incorporating additional quantitative or mixed-method approaches. Furthermore, the sample, consisting only of students and teachers from one university, may not adequately represent the diverse experiences and perceptions of online learning across different educational contexts. These limitations suggest the need for a cautious interpretation of the findings and indicate areas for future research expansion. Future research could extend this study by incorporating a larger, more diverse sample to gain a broader understanding of undergraduate students’ retrospections across different contexts and cultures. Furthermore, research could also explore how to better equip students with the skills and strategies necessary to optimize their online learning experiences, especially in terms of the self-regulated learning and motivation aspects.

Data availability

The data supporting this study is available from https://doi.org/10.6084/m9.figshare.25583664.v1 . The data consists of reflective diaries from 46 Year 1 students from a comprehensive university in China and 18 college teachers. We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. The results highlight the critical need for tailored online learning strategies and provide insights into its advantages and challenges for stakeholders in higher education.

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The Corresponding author received the National Social Science Foundation of China for Education General Program (BGA210054) for this work.

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XSX was responsible for conceptualization and, alongside YFZ, for data curation. YJS and XYX conducted the formal analysis. Funding acquisition was managed by YFZ. The investigation was carried out by YJS and YFZ. Methodology development was a collaboration between YJS and XSX. XSX and YJS also managed project administration, with additional resource management by SSH and XYX. YJS handled the software aspect, and supervision was overseen by XSX. SSH and XYX were responsible for validation, and visualization was managed by YJS. The original draft was written by XSX and YJS, while the review and editing were conducted by YFZ and SSH.

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Su, Y., Xu, X., Zhang, Y. et al. Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors. Humanit Soc Sci Commun 11 , 594 (2024). https://doi.org/10.1057/s41599-024-03097-z

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A Systematic Review of the Research Topics in Online Learning During COVID-19: Documenting the Sudden Shift

• Min Young Doo Kangwon National University http://orcid.org/0000-0003-3565-2159
• Meina Zhu Wayne State University
• Curtis J. Bonk Indiana University Bloomington

Since most schools and learners had no choice but to learn online during the pandemic, online learning became the mainstream learning mode rather than a substitute for traditional face-to-face learning. Given this enormous change in online learning, we conducted a systematic review of 191 of the most recent online learning studies published during the COVID-19 era. The systematic review results indicated that the themes regarding “courses and instructors” became popular during the pandemic, whereas most online learning research has focused on “learners” pre-COVID-19. Notably, the research topics “course and instructors” and “course technology” received more attention than prior to COVID-19. We found that “engagement” remained the most common research theme even after the pandemic. New research topics included parents, technology acceptance or adoption of online learning, and learners’ and instructors’ perceptions of online learning.

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

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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.

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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.

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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.

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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

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

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Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

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