robotics research titles for high school students

150+ Easy Robotics Research Topics For Engineering Students In 2024

Learning about robots and how they work is really interesting. It involves using new and advanced technology. Robots are made by combining different types of engineering and smart computer programs. This blog talks about how robots communicate, explains the basics of robotics, and shows how important it is for students. We help students choose from 150+ topics about robots that are easy to understand and study in 2024.

We cover a wide range of topics, from how robots think and interact with people to working together in groups and the moral questions involved. We talk about why studying robots is good, the problems students might face, and suggest five great research topics for success in school. Stick around with us to learn a lot about the exciting world of Robotics Research Topics research!

What Is Robotics?

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The goal of robotics is to build devices that are capable of autonomous tasks. These machines are designed to do things that humans can’t or prefer not to do. They are made to work in different places, from the deep sea to outer space. These robots can have arms, wheels, sensors, and computers that help them move and think.

Robots can do numerous tasks, from assembling cars in factories to exploring distant planets. They can assist in surgeries, clean floors, or even deliver packages. The field of robotics involves designing, building, and programming these machines to perform specific tasks, making our lives easier and sometimes even safer.

Importance And Impact Of Robotics Research In Student’s Life

Here are some importance and impact of robotics research in students’s life:

1. Skill Development

Robotics research allows students to develop crucial skills like problem-solving, critical thinking, and creativity. It challenges them to think innovatively, design solutions, and apply theoretical knowledge into practical scenarios, fostering a hands-on learning experience.

2. Future Career Opportunities

Engaging in robotics research equips students with skills highly sought after in various industries. Understanding robotics opens doors to diverse career opportunities in fields like engineering, technology, healthcare, and even entrepreneurship, preparing students for the job market of the future.

3. Technological Advancements

Through research, students contribute to the advancement of technology. Their discoveries and innovations in robotics research can lead to breakthroughs, new inventions, and improvements in existing systems, benefiting society and shaping the future.

4. Problem Solving and Innovation

Robotics research challenges students to solve real-world problems creatively. It encourages them to think outside the box, invent new solutions, and create technologies that can positively impact society, fostering a mindset for innovation.

5. Personal Development

Engagement in robotics research boosts students’ confidence, fostering a sense of achievement and a willingness to take on new challenges. It encourages self-motivation, perseverance, and adaptability, shaping well-rounded individuals ready to tackle future endeavors.

Tips For Choosing The Right Robotics Research Topics

Here are some tips for choosing the right robotics research topics: 

Tip 1: Follow Your Passion

Choose a robotics research topic that excites and interests you. When you’re passionate about the subject, you’ll stay motivated throughout the research process, making it easier to explore and understand the complexities of the topic.

Tip 2: Assess Available Resources

Consider the resources available to you, such as access to equipment, tools, and expert guidance. Select a topic that aligns with the available resources to ensure you can conduct your research effectively and efficiently.

Tip 3: Relevance and Impact

Opt for a robotics research topic that has real-world relevance and potential impact. Focusing on topics that address current problems or future technological advancements can make your research more meaningful and valuable.

Tip 4: Scope and Manageability

Pick a subject that is in between too wide and too specific. Ensure it’s manageable within the given time frame and resources, allowing you to explore and delve deep into the subject without overwhelming yourself.

Tip 5: Consult with Mentors and Peers

Discuss potential research topics with mentors or peers. Seeking advice and feedback can provide valuable insights, helping you refine and select the most suitable and intriguing robotics research topic.

In this section, we will provide 150+ robotics research topics for engineering students:

I. Artificial Intelligence and Robotics

• Cognitive Robotics: Emulating Human Thought Processes
• Ethical Implications of AI in Autonomous Robotics
• Reinforcement Learning Algorithms in Robotics
• Explainable AI in Robotics: Ensuring Transparency
• Deep Learning Techniques for Object Recognition in Robotics
• AI-Enabled Medical Robotics for Enhanced Healthcare
• AI-Driven Social Robotics for Improved Interaction
• Evolution of AI in Self-driving Vehicles
• Robotics as a Tool for AI Education in Schools
• Integrating AI with Robotics for Enhanced Predictive Capabilities

II. Human-Robot Interaction

• Emotional Intelligence in Human-Robot Interaction
• Impact of Social Robotics in Elderly Care
• Personalization in Human-Robot Interaction
• Enhancing Trust and Communication in Human-Robot Relationships
• Cultural Adaptation in Human-Robot Interaction
• The Role of Ethics in Human-Robot Interaction Design
• Non-verbal Communication and Gestures in Human-Robot Interaction
• Augmented Reality and Human-Robot Collaboration
• Designing User-Friendly Interfaces for Robotic Interaction
• Evaluating User Experience in Human-Robot Interaction Scenarios

III. Swarm Robotics

• Swarm Robotics in Surveillance and Security
• Dynamic Task Allocation in Swarm Robotics
• Emergent Behavior in Swarm Robotics Systems
• Cooperative Swarm Robotic Systems in Environmental Cleanup
• Bio-inspired Swarm Robotics: Learning from Nature
• Coordination and Communication Protocols in Swarm Robotics
• Optimization Algorithms for Swarm Robotic Systems
• Swarm Robotics in Underground Mining Operations
• Robotic Swarms for Disaster Response and Rescue Missions
• Challenges in Scalability of Swarm Robotic Networks

IV. Soft Robotics

• Bio-inspired Soft Robotic Grippers for Delicate Object Handling
• Soft Robotics in Biomedical Applications
• Wearable Soft Robotics for Rehabilitation and Assistance
• Soft Robotics for Prosthetics and Exoskeletons
• Advancements in Soft Robotic Material Science
• Adaptive Soft Robots for Unstructured Environments
• Designing Soft Robots for Underwater Exploration
• Challenges in Control and Sensing in Soft Robotics
• Soft Robotic Actuators and Sensors
• Soft Robotics in Food and Agriculture Industry Innovations

V. Autonomous Navigation and Mapping

• Simultaneous Localization and Mapping (SLAM) in Autonomous Vehicles
• Advances in LIDAR and Radar Technologies for Navigation
• Mapping and Navigation Techniques in GPS-denied Environments
• Robustness of Autonomous Navigation in Dynamic Environments
• Learning-based Approaches for Adaptive Autonomous Navigation
• Ethics and Legalities in Autonomous Navigation Systems
• Human Safety in Autonomous Vehicles and Navigation
• Multi-modal Sensor Fusion for Precise Navigation
• Challenges in Weather-Adaptive Navigation for Autonomous Systems
• Social and Ethical Implications of Autonomous Navigation in Urban Environments

VI. Robotic Vision and Perception

• Object Detection and Recognition in Robotic Vision Systems
• Enhancing Robotic Vision through Deep Learning
• Perception-based Grasping and Manipulation in Robotics
• Visual SLAM for Indoor and Outdoor Robotic Navigation
• Challenges in Real-time Object Tracking for Robotics
• Human-Centric Vision Systems for Social Robots
• Ethics of Visual Data and Privacy in Robotic Vision
• Advancements in 3D Vision Systems for Robotics
• Vision-based Localization and Mapping for Mobile Robots
• Vision and Perception Challenges in Unstructured Environments

VII. Robot Learning and Adaptation

• Reinforcement Learning for Robotic Control and Decision-making
• Transfer Learning for Robotics in Real-world Environments
• Adaptive Learning Algorithms for Robotic Systems
• Continual Learning and Long-term Adaptation in Robots
• Ethical Considerations in Robot Learning and Autonomy
• Learning-based Techniques for Human-robot Collaboration
• Challenges in Unsupervised Learning for Robotic Applications
• Lifelong Learning in Robotic Systems
• Balancing Stability and Exploration in Robot Learning
• Learning Robotic Behavior through Interaction and Imitation

VIII. Robotic Manipulation and Grasping

• Dexterity and Precision in Robotic Manipulation
• Grasping Strategies for Varied Objects in Robotics
• Multi-fingered Robotic Hands and Adaptive Grasping
• Haptic Feedback for Enhanced Robotic Grasping
• Challenges in Grasping Fragile and Deformable Objects
• Grasping and Manipulation in Cluttered Environments
• Learning-based Approaches for Adaptive Grasping
• Robotic Manipulation for Assembly and Manufacturing
• Human-Robot Collaboration in Grasping Tasks
• Ethical Considerations in Robotic Manipulation and Grasping

IX. Robotic Sensing and Sensory Integration

• Sensor Fusion Techniques for Comprehensive Robot Perception
• Role of LIDAR, RADAR, and Cameras in Robotic Sensing
• Challenges in Sensor Data Integration for Robotic Decision-making
• Ethical Implications of Sensory Data Collection in Robotics
• Tactile Sensing and Haptic Feedback in Robotic Systems
• Multi-modal Sensing for Robotic Perception in Dynamic Environments
• Role of Environmental Sensors in Autonomous Robotics
• Neural Networks for Sensor Data Interpretation in Robotics
• Sensor Calibration and Accuracy in Robotic Systems
• Sensory Integration Challenges in Unstructured Environments

X. Multi-Robot Systems and Coordination

• Coordination Mechanisms in Heterogeneous Multi-robot Systems
• Cooperative Task Allocation in Multi-robot Systems
• Communication Protocols in Multi-robot Coordination
• Role of AI in Dynamic Multi-robot Collaboration
• Challenges in Scalability and Robustness of Multi-robot Systems
• Ethics and Security in Multi-robot Networked Systems
• Hierarchical and Decentralized Approaches in Multi-robot Systems
• Multi-robot Systems in Infrastructure Maintenance and Inspection
• Collaborative Multi-robot Systems for Search and Rescue Missions
• Learning-based Coordination in Swarms of Robots

XI. Robot Ethics and Governance

• Ethical Decision-making in Autonomous Robotics
• Legal and Ethical Frameworks for Robotic Systems
• Accountability and Transparency in Robotic Decision-making
• Ethical Implications of AI in Robotic Systems
• Ensuring Fairness and Bias Mitigation in Robotic Algorithms
• Ethical Considerations in Robotic Assistive Technologies
• Designing Ethical Guidelines for Human-Robot Interaction
• Governance of Robotic Systems in Public Spaces
• Robotic Data Privacy and Security: Ethical Perspectives
• Societal Impact and Responsibility in the Development of Robotic Technologies

XII. Robotic Assistive Technologies

• Robotics in Prosthetics and Rehabilitation
• Assistive Robotics for Elderly and Disabled Individuals
• Human-Centric Design in Assistive Robotic Devices
• Social and Psychological Impact of Assistive Robotics
• Robotics in Cognitive and Physical Therapy
• Customization and Personalization in Assistive Technologies
• Challenges in Implementing Assistive Robotics in Healthcare
• Ethical and Legal Considerations in Assistive Robotics
• Continuous Learning and Adaptation in Assistive Robots
• Human Empowerment through Assistive Robotic Devices

XIII. Robotics in Healthcare and Medical Applications

• Surgical Robotics: Advancements and Future Prospects
• Robotics in Telemedicine and Remote Healthcare
• Robotics in Drug Delivery and Therapy
• Robotics in Imaging and Diagnosis in Medicine
• Ethical Considerations in Robotic Medical Procedures
• Assistive Robotics in Hospitals and Healthcare Facilities
• Robotic Technologies in Emergency Response and Medical Rescue
• Robotics in Rehabilitation and Physical Therapy
• Human-Robot Collaboration in Healthcare Settings
• Challenges and Future Trends in Robotic Healthcare Applications

XIV. Robotics Research Topics for High School Students

• Introduction to Basic Robotic Programming and Control
• Exploring Simple Robotic Mechanisms and Prototyping
• Designing and Building Miniature Robotic Vehicles
• Understanding the Basics of Robotic Sensors and Actuators
• Introduction to Ethical Considerations in Robotics
• Robotics in Everyday Life: Applications and Implications
• Introduction to Human-Robot Interaction and Safety
• Introduction to the World of AI and ML in Robotics
• Robotics in Environmental Conservation and Sustainability
• Career Prospects and Opportunities in Robotics for High School Students

XV. Robotics Research Topics for STEM Students

• Advanced Programming in Robotics: Algorithms and Applications
• Design and Development of Autonomous Robotic Systems
• Innovations in Bio-inspired Robotics: Learning from Nature
• Data Science and AI Integration in Robotics
• Robotics and Industry 4.0: Future Trends and Transformations
• Advanced Control Systems for Robotic Manipulation
• Robotics and Ethics: Societal Impact and Responsibilities
• Robotics in Space Exploration and Astronaut Assistance
• Robotic Vision and Perception: Deep Dive into Sensing Technologies
• Advanced Topics in Swarm Robotics and Multi-Robot Coordination
• The Impact of Robotics in Aerospace Industry Advancements

Read More 

• Robotics Project Ideas
• Programming Languages For Robotics

Benefits Of Working On Robotics Research Topics

Here are some benefits of working on robotics research topics:

1. Practical Application

Working on robotics research topics allows individuals to apply theoretical knowledge to practical scenarios. It bridges the gap between learning in classrooms and real-world implementation, offering hands-on experience and a deeper understanding of concepts.

2. Skill Enhancement

Engagement in robotics research topics hones various skills like problem-solving, critical thinking, and teamwork. It fosters creativity, technical proficiency, and the ability to innovate, preparing individuals for diverse challenges in their academic and professional lives.

3. Career Development

Working on robotics research topics enhances one’s career prospects. It equips individuals with sought-after skills in industries like engineering, technology, and research, opening doors to diverse career opportunities and establishing a strong foundation for future professional growth.

4. Contribution to Innovation

Robotics research allows individuals to contribute to innovation. Their findings and discoveries may lead to technological advancements, new inventions, and improved methodologies, shaping the future landscape of robotics and its applications.

5. Problem-Solving and Creativity

Engaging in robotics research encourages individuals to think creatively and find solutions to real-world problems. It cultivates an environment where individuals can explore new ideas, tackle challenges, and contribute to advancements in the field of robotics.

Challenges Face By Students During Robotics Research

Students often face limitations in accessing necessary resources, such as advanced hardware and software. The complexity of problem-solving within robotics requires high-level analytical skills , and the rapidly evolving nature of technology demands constant adaptability. 

• Resource Limitations: Inadequate access to cutting-edge hardware and software can impede the experimentation and implementation phases of robotics research.
• Complex Problem-solving : Tackling intricate technical issues within robotics demands high levels of analytical skills and critical thinking.
• Adaptability to Technological Changes: Keeping pace with rapidly evolving technology in the robotics field presents a consistent challenge for students.
• Theory-Practice Integration: Bridging the gap between theoretical knowledge and practical application poses difficulties in robotics research.
• Time Constraints: Meeting project deadlines while ensuring quality research and development often creates pressure for students.
• Interdisciplinary Knowledge: Robotics research necessitates a blend of engineering, computer science, mathematics, and AI, which can be challenging to integrate.
• Trial and Error Process: Experiments may result in failures, requiring an iterative approach and patience during the research and development process.

Bonus Tip: 5 Must-Have Things For Robotics Research Titles to Achieve High Scores

• Clarity and Precision: Ensure the title clearly conveys the essence of your research topic without ambiguity.
• Captivating and Engaging Language: Craft a title that sparks interest and draws attention to the significance of your robotics research.
• Reflect Innovation and Novelty: Highlight the originality and innovative aspects of your research to captivate the audience.
• Incorporate Relevant Keywords : Use specific and relevant keywords to make your title easily discoverable and reflect your research area.
• Reflect the Core Purpose: Ensure your title encapsulates the primary focus of your robotics research, providing a glimpse of its importance and relevance.

Robotics research presents an exciting journey, from understanding the transactional communication model to exploring the vast world of robotics. This exploration emphasizes the pivotal role of robotics in students’ lives, offering guidance on choosing appropriate research topics. With over 150 easy-to-pick ideas for aspiring engineers in 2024, it covers crucial areas like AI, human-robot interaction, and ethical considerations. 

Moreover, highlighting benefits such as skill development and career opportunities, it also acknowledges the challenges students face during research. Overall, this comprehensive guide caters to high school and STEM students, concluding with valuable tips for crafting compelling robotics research titles, enhancing the learning experience.

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200+ Great Robotics Research Topics For High School Students [2024 Updated]

Have you ever thought about all the amazing things you could discover in the world of robotics? It’s like unlocking endless possibilities! And guess what? There’s a whole bunch of incredible Robotics Research Topics for high school students like you to explore.

After getting experience with various robotics research topics, I found that it’s like building robots that can assist in healthcare, exploring how robots learn from humans, or even investigating how they can help in environmental conservation. These topics aren’t just for the big scientists; they’re perfect for high school students with a passion for technology and innovation.

You could dive into the world of artificial intelligence and robotics, understanding how these amazing machines learn and make decisions. Or, if you’re more interested in the role of robots in space exploration, that’s a fascinating field to explore, too!

From designing robots to perform specific tasks to understanding their impact on our daily lives, the field of robotics offers exciting research topics for high school students. So, if you’re ready to experiment and discover the incredible world of robotics, let’s dive into these research topics and uncover wonders together!

You May Also Like: PhD Research Topics In Artificial Intelligence

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What Is Robotics Research Topics For High School Students

“What is robotics research topics for high school students” refers to exploring and identifying various areas of study and investigation within robotics that are specifically tailored for high school students. It involves discovering topics, ideas, and projects related to robotics that are suitable for students at a high school level of understanding and engagement.

This phrase highlights the search for intriguing subjects within robotics that align with the interests, skills, and capabilities of high school students. It emphasizes the potential for young minds to explore, experiment, and delve into the diverse field of robotics through research and projects that cater to their level of knowledge and curiosity.

How Do I Choose Good Robotics Research Topics For High School Students

Picking the right robotics research topics for high school can be exciting! Here’s a simple guide to help you choose:

• Interest and Passion: Start with what excites you. Are you passionate about healthcare, space exploration, or environmental issues? Find topics in robotics related to these areas.
• Accessibility: Consider topics that you can explore with available resources. Can you build a simple robot at home? Can you access materials or software for your chosen topic?
• Relevance: Look for topics that connect to real-world problems or advancements in robotics. This could be in fields like medicine, education, or industry.
• Complexity: Choose a topic that challenges you but isn’t overwhelmingly difficult. It should be something you can research and understand at your level.
• Guidance and Support: Ensure there are resources or mentors available to guide you. This could be a teacher, an online community, or local robotics clubs.
• Wonder: Seek topics that haven’t been extensively explored. This allows for fresh ideas and potential for innovative solutions.
• Experimentation: Opt for topics that allow hands-on experimentation. Robotics is about tinkering and testing ideas, so choose a topic that allows for practical application.

What Are The 7 Biggest Challenges In Robotics?

Here are the several challenges that persist in the field of robotics, influencing the development and application of robots.

Ensuring robots operate safely in various environments, especially when working alongside humans. Developing safety protocols and systems to prevent accidents and mitigate risks.

2. Autonomy and AI

Enhancing robots’ ability to make autonomous decisions, adapt to dynamic environments, and learn from experiences without human intervention. Advancing AI capabilities for improved decision-making and problem-solving.

3. Human-Robot Interaction (HRI)

Improving the seamless interaction between robots and humans, including natural language processing, understanding human gestures, and designing robots that can perceive and respond appropriately to human emotions and intentions.

4. Dexterity and Manipulation

Developing robots with fine motor skills and dexterity to manipulate objects delicately and perform complex tasks similar to human capabilities.

5. Versatility and Adaptability

Creating robots that can perform multiple tasks across various domains efficiently. Designing adaptable robots capable of handling diverse environments and tasks without extensive reprogramming.

6. Ethical and Social Implications

Addressing ethical dilemmas concerning the use of robots, such as job displacement, privacy concerns, and the ethical boundaries of AI and autonomous decision-making in critical situations.

7. Cost and Accessibility

Making robotics technology more affordable, accessible, and easier to implement across different industries and applications, ensuring broader adoption and utilization.

List of 171+ Great Robotics Research Topics For High School Students

Here are the most interesting robotics research topics for high school students. 

Basics of Robotics Research Topics For High School Students

• History and Evolution of Robotics
• Fundamentals of Robot Design and Construction
• Understanding Sensors and Actuators in Robotics
• Programming Basics for Robotics
• Robot Control Systems: Types and Applications

Applications of Robotics Research Topics

• Robotics in Everyday Life: Home Automation Systems
• Robotics in the Healthcare Industry
• Robotics in Manufacturing and Industrial Automation
• The Role of Robotics in Space Exploration
• Robotics in Agriculture: Precision Farming and Automated Techniques
• Robotics in Education: Enhancing Learning with Robotic Systems
• Robots in Entertainment and Gaming
• Robotics in Environmental Conservation and Sustainability
• Robotics in Disaster Response and Management
• Robotics in Transportation: Autonomous Vehicles and Drones

Advanced Robotics Research Topics For Students

• Artificial Intelligence and Machine Learning in Robotics
• Human-Robot Interaction and Collaboration
• Swarm Robotics: Cooperation Among Multiple Robots
• Soft Robotics: Developing Flexible and Adaptive Machines
• Bio-inspired Robotics: Learning from Nature
• Exoskeleton Robotics: Enhancing Human Strength and Mobility
• Robotics Ethics and Moral Implications
• Robotics in Sports: Enhancing Athletic Performance
• Social and Emotional Robotics: Developing Empathetic Machines
• Robotics and Brain-Computer Interfaces

Robotics Research Topics For College Students

• Robotic Prosthetics: Advancements in Artificial Limbs
• Robotics in Orthopedic Surgery: Innovations and Impact
• Robotics in Neurosurgery: Challenges and Future Prospects
• Robotics in Cardiac Surgery and Intervention
• Robotics in Cancer Treatment: Targeted Therapies and Precision Medicine
• Robotics in Rehabilitation: Assisting Individuals with Disabilities
• Robotics in Aerospace Engineering: Designing Robotic Spacecraft
• Underwater Robotics: Exploring Marine Environments
• Robotics in Archaeology and Cultural Heritage Preservation
• Robotics in Law Enforcement and Search & Rescue Operations

Emerging Technologies in Robotics Research Topics For High School Students

• Quantum Robotics: Exploring Quantum Computing in Robotics
• Nanorobotics: Applications in Medicine and Beyond
• Brain-Computer Interface Controlled Robots
• Augmented Reality and Robotics Integration
• 3D Printing in Robotics: Advancements and Applications
• Robotic Swarms for Environmental Monitoring
• Robotics and Blockchain: Innovations and Challenges
• Robotic Vision Systems: Enhancing Perception and Object Recognition
• Robotic Ethics and Laws: Shaping Future Regulations

Easy Robotics Research Topics For Society

• Impact of Robotics on Employment and Workforce
• Accessibility in Robotics: Designing Inclusive Technologies
• Robotics and Economic Implications
• Robotic Surveillance and Privacy Concerns
• Robotics in Education: Integrating Robotics into School Curriculum
• Robotic Assistance for Elderly Care and Aging Population
• Ethical Considerations of Autonomous Robots in Warfare
• Social Perception of Robots: Cultural Attitudes and Challenges
• Robotic Innovation and Intellectual Property Rights

Future Trends in Robotics Research Topics For High School Students

• Robotics and Future Smart Cities
• Autonomous Robots and the Future of Transportation
• Ethical Considerations in Developing AI for Robotics
• Robotics in the Fourth Industrial Revolution
• Robotics and Sustainable Development Goals (SDGs)
• Challenges and Prospects of Space Robotics
• Ethics of AI and Robotics Integration
• Future Perspectives of Robotic-Assisted Living

Robotics Research Topics For High School Students In Artificial Intelligence

• Deep Learning in Robotics: Applications and Challenges
• Reinforcement Learning for Autonomous Robotics
• Natural Language Processing in Human-Robot Interaction
• Explainable AI in Robotics: Ensuring Transparency and Accountability
• Cognitive Robotics: Understanding Human-Like Thought Processes

Robotics in Industry and Automation

• Robotic Process Automation (RPA) in Business Operations
• Robotics and the Future of Work: Impact on Job Markets
• Robotics in Supply Chain Management and Logistics
• Human-Robot Collaboration in Manufacturing
• Robotics and Lean Manufacturing: Improving Efficiency and Quality

Robotics in Education and Learning

• Robotics Clubs in Schools: Benefits and Challenges
• Integrating Robotics into STEM Education
• Robotics in Online Learning Platforms
• Robotics as a Tool for Special Education
• Building Low-Cost Educational Robots for Learning Purposes

Best Robotics Research Topics For High School Students In Hardware and Design

• Innovations in Robotic Grippers and Manipulators
• Advancements in Robotic Vision Systems
• Developing Energy-Efficient Robots: Challenges and Solutions
• Modular Robotics: Building Versatile and Adaptable Systems
• Wearable Robotics: Enhancing Human Abilities

Robotics in Health and Medicine

• Robotics in Drug Delivery: Targeted and Controlled Therapies
• Teleoperated Surgical Robotics: Advancements and Limitations
• Robotics in Home Healthcare Assistance
• Socially Assistive Robots for Mental Health Support
• Robotic Rehabilitation: Therapeutic Applications and Success Stories

Robotics and Environmental Sustainability

• Robotics for Monitoring Air and Water Quality
• Robot-Assisted Recycling and Waste Management
• Autonomous Drones for Wildlife Conservation
• Robotics for Disaster Recovery and Environmental Cleanup
• Sustainable Materials in Robotic Construction

Robotic Ethics and Governance

• Ethical Dilemmas in Autonomous Decision-Making by Robots
• Regulatory Frameworks for Autonomous Robotics
• Robotic Laws and Liability: Determining Responsibility
• Ethical Considerations of Robot Rights and Personhood
• Ensuring Fairness and Bias Mitigation in AI-Driven Robots

Robotics and Human-Robot Interaction

• Emotional Intelligence in Robots: Empathy and Emotional Response
• Understanding Trust in Human-Robot Relationships
• Designing User-Friendly Interfaces for Robots
• Robotic Companionship for Loneliness and Social Isolation
• Cultural Adaptation of Robots: Challenges and Solutions

Advanced Robotics Technologies

• Quantum Robotics: Applications Beyond Computing
• Swarm Intelligence in Robot Swarms: Behavior and Coordination
• Soft Robotics: Innovations in Flexible and Elastic Structures
• Robotic Telepresence: Enabling Remote Interaction
• Self-Healing Robotics: Materials and Applications

Robotics Research Topics For High School Students in Entertainment and Arts

• Interactive Robotic Art Installations
• Robotics in Theatre and Performance Arts
• Robotic Instruments and Music Composition
• Augmented Reality in Robotic Storytelling and Gaming
• Robotics and Virtual Reality Experiences

Robotics Research Topics In Sports

• Robotics in Sports Training and Performance Analysis
• Robotic Umpires and Referees: Implications in Sports
• Exoskeletons in Athletic Training and Injury Rehabilitation
• Wearable Robotics for Paralympic Athletes
• Autonomous Robotics in Sports Event Management

Robotics Research Topics For High School Students In Agriculture

• Precision Agriculture with Robotic Crop Monitoring
• Agricultural Drones for Pest Control and Crop Spraying
• Robotic Harvesting: Improving Efficiency in Agriculture
• AI in Greenhouse Robotics for Optimal Plant Growth
• Robotics in Aquaculture and Fish Farming

Robotics in Retail and Customer Service

• Robotic Store Assistants and Customer Interaction
• Inventory Management with Robotics and AI
• Robotic Delivery Systems and Last-Mile Logistics
• Personalized Shopping Experiences with Robotic Assistants
• Robotics in E-commerce Fulfillment Centers

Robotics and Law Enforcement

• Police Robotics: Surveillance and Crime Prevention
• Search and Rescue Robots in Emergency Response
• Autonomous Patrol Drones for Public Safety
• Robotics in Forensic Investigations
• Robotic Tools for Bomb Disposal Units

Robotics and Fashion Industry

• Robotic Fabrication and 3D Printing in Fashion Design
• Wearable Tech and Robotics in Fashion Shows
• Robotics in Textile Production and Garment Manufacturing
• Robotic Tailoring: Customizing Clothing with Automation
• Sustainable Fashion with Robotics and AI

Robotics and Social Issues

• Robotics in Refugee Assistance and Humanitarian Aid
• Robotics for Elderly Care and Aging Population Support
• Robotic Solutions for Homelessness and Poverty
• Ethical Considerations in Deploying Robots in Developing Countries
• Empowering Marginalized Communities with Robotics

Robotics and Transportation

• Urban Mobility Solutions with Autonomous Vehicles
• Robotic Public Transportation Systems
• AI-Powered Traffic Management and Control
• Drone Delivery Services for Urban Areas
• Hyperloop Technology and Robotic Infrastructure

Cool Gaming Robotics Research Topics For High School Students

• AI-Driven NPCs (Non-Playable Characters) in Video Games
• Robotic Augmented Reality Gaming Experiences
• Gesture and Motion Control Gaming with Robotics
• Robotics in Virtual Reality Gaming Platforms
• AI Dungeon Master: Robotic Assistance in Tabletop Games

Robotics in Legal and Ethical Dilemmas

• Robotic Ethics and the Trolley Problem
• AI Bias and Ethics in Robotic Decision-Making
• The Morality of Robot Rights and Responsibilities
• Ethical Considerations of Lethal Autonomous Weapons
• Robotic Surveillance and Privacy Rights

Robotics and Social Integration

• Social Robotics for Assisting Individuals with Autism
• Robotic Language Learning Companions for Children
• Robots as Assistants in Social Integration Programs
• Robotic Support for Mental Health and Well-being
• Robotics in Refugee Education and Integration

Robotics and Financial Technology

• Robotics in Financial Forecasting and Market Analysis
• Robo-Advisors: Automation in Investment and Wealth Management
• Robotics in Banking: Improving Customer Service
• AI-Powered Risk Management in Financial Institutions
• Automation in Cryptocurrency Trading and Management

Robotics and Cybersecurity

• Robotics in Cyber Threat Detection and Prevention
• Robotic Solutions for Cyber Attacks and Intrusion Detection
• AI-Powered Robotic Defense Systems
• Ethical Hacking with Robotics and AI
• Secure Robotics: Ensuring Protection from Malicious Attacks

Robotics in Education and Skill Development

• Robotics-based Coding Programs for Elementary School Students
• Robotics and Critical Thinking Skills Development
• Robotics Competitions: Impact on Skill Enhancement
• Robotics Clubs and Extracurricular Learning Opportunities
• Robotics as a Tool for Enhancing Problem-Solving Abilities

Robotics and Remote Sensing

• Remote Sensing Applications Using Robotics
• Robotic Systems for Weather Forecasting and Prediction
• Monitoring Natural Disasters with Robotics
• Using Drones for Environmental Monitoring and Analysis
• Robotics in Geographical Information Systems (GIS)

Robotics in Food Industry

• Robotics in Food Processing and Packaging
• Automated Kitchen Appliances and Culinary Robots
• Robotics for Quality Control in Food Manufacturing
• Robotics in Agricultural Harvesting and Sorting
• Sustainable Food Production with Robotic Technology

Robotics in Psychological Research

• Robotic Models for Psychological Experiments
• Human-Robot Interaction in Psychological Therapy
• Robotics for Studying Human Behavior and Reactions
• Robotic Models for Cognitive Development Studies
• Robotic Tools in Behavioral Therapy and Intervention

Robotics in Tourism and Hospitality

• Service Robots in Hotels and Hospitality Industry
• Robotic Tour Guides for Cultural and Heritage Sites
• Autonomous Robotic Concierges in Travel and Tourism
• Robotics in Cruise Ship Services and Operations
• Robotics in Theme Park Entertainment and Attractions

Robotics Research Topics For High School Students In Energy

• Robotics in Renewable Energy Production
• Robotic Systems for Solar Panel Maintenance
• AI-Powered Robotics in Energy Distribution
• Robotics in Oil and Gas Exploration and Maintenance
• Smart Grid Management Using Robotic Technologies

Robotics and Fashion Design

• Robotics in Fashion Runway Shows and Presentations
• AI-Driven Fashion Designing with Robotics
• Robotic Fabric Cutting and Sewing Techniques
• Wearable Tech and Robotics in Fashion Design
• Robotics in Textile Printing and Pattern Creation

Robotics Research Topics For High School Students For Music

• Robotic Musical Instruments and Composers
• AI-Powered Music Creation with Robotics
• Robotic Conductor Systems for Orchestras
• Robotics in Sound Production and Studio Mixing
• Interactive Robotic Music Performances and Installations

Good Robotics Research Topics For High School Students For Sports Training

• Biomechanics Analysis Using Robotic Systems
• Robotics in Athlete Performance Enhancement
• Robotic Coaches and Training Assistants in Sports
• AI-Powered Personalized Training with Robotics
• Robotic Systems in Sports Injury Prevention and Recovery

Robotics in Marketing and Advertising

• Robotic Customer Service Representatives in Marketing
• Robotic Solutions for Advertising and Product Display
• AI-Powered Robotic Marketing Strategies
• Robotics in Data Analysis for Market Research
• Automated Sales and Promotion Using Robotics

Recent Robotics Research Topics For High School Students For Mind-Body Wellness

• Robotics in Meditation and Relaxation Techniques
• Robotic Tools for Stress Management and Mental Health
• AI-Driven Wellness Coaches and Assistants
• Robotics in Yoga and Physical Well-being
• Robotic Systems for Sleep Improvement and Health Monitoring

Amazing Robotics Research Topics For High School Students PDF

What are some current topics of research in the field of robotics.

Here’s a table summarizing various current research topics in robotics:

Conclusion 

Robotics Research Topics for High School Students offer an exciting gateway into the captivating realm where creativity meets technology. These diverse topics serve as launchpads for curious minds, paving the way for exploration, innovation, and hands-on learning.

Exploring Bio-Inspired Robotics allows a glimpse into nature’s designs, while delving into Human-Robot Interaction leads to crafting robots that understand human emotions. Biomedical Robotics presents an opportunity to revolutionize healthcare, and Space Robotics offers an adventure beyond the skies.

These topics transcend mere robotics; they tackle real-world challenges. Imagine designing disaster-response robots in Field Robotics or crafting safer, softer robots in Soft Robotics. Ethical considerations in Robot Ethics and the intricacies of Multi-Robot Systems reveal insights into collective intelligence.

Engaging with these topics isn’t just about building robots; it’s about acquiring invaluable skills like coding, problem-solving, and teamwork. It’s about resilience forged through trial and error, the joy of creating something novel, and the fulfillment of making a meaningful impact.

What Are The Four 4 Types Of Robotics?

The four types of robotics are industrial robots, service robots, collaborative robots (cobots), and mobile robots.

What Are The 5 Major Fields of Robotics?

The five major fields of robotics are industrial robots, medical robots, service robots, military robots, and entertainment robots.

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200+ Robotics Research Topics: Discovering Tomorrow’s Tech

• Post author By admin
• September 15, 2023

Explore cutting-edge robotics research topics and stay ahead of the curve with our comprehensive guide. Discover the latest advancements in the field today.

Robotics research topics are not like any other research topics. In these topics science fiction meets reality and innovation knows no bounds.

In this blog post we are going to explore some of the best robotics research topics that will help you to explore machine learning, artificial intelligence and many more.

Apart from that you will also explore the industries and the future of robotics. Whether you are an experienced engineering or a student of robotics, these project ideas will definitely help you to explore a lot more the dynamic and ever evolving world of robotics. So be ready to explore these topics:-

Table of Contents

Robotics Research Topics

Have a close look at robotics research topics:-

Autonomous Robots

• Development of an Autonomous Delivery Robot for Urban Environments
• Swarm Robotics for Agricultural Crop Monitoring and Maintenance
• Simultaneous Localization and Mapping (SLAM) for Indoor Navigation of Service Robots
• Human-Robot Interaction Study for Improved Robot Assistance in Healthcare
• Self-Driving Car Prototype with Advanced Perception and Decision-Making Algorithms
• Autonomous Aerial Surveillance Drones for Security Applications
• Autonomous Underwater Vehicles (AUVs) for Ocean Exploration
• Robotic Drones for Disaster Response and Search-and-Rescue Missions
• Autonomous Exploration Rover for Planetary Surfaces
• Unmanned Aerial Vehicles (UAVs) for Precision Agriculture and Crop Analysis

Robot Manipulation and Grasping

• Object Recognition and Grasping Planning System for Warehouse Automation
• Cooperative Multi-Robot Manipulation for Assembly Line Tasks
• Tactile Sensing Integration for Precise Robotic Grasping
• Surgical Robot with Enhanced Precision and Control for Minimally Invasive Surgery
• Robotic System for Automated 3D Printing and Fabrication
• Robot-Assisted Cooking System with Object Recognition and Manipulation
• Robotic Arm for Hazardous Materials Handling and Disposal
• Biomechanically Inspired Robotic Finger Design for Grasping
• Multi-Arm Robotic System for Collaborative Manufacturing
• Development of a Dexterous Robotic Hand for Complex Object

Robot Vision and Perception:

• Object Detection and Recognition Framework for Robotic Inspection
• Deep Learning-Based Vision System for Real-time Object Recognition
• Human Activity Recognition Algorithm for Assistive Robots
• Vision-Based Localization and Navigation for Autonomous Vehicles
• Image Processing and Computer Vision for Robotic Surveillance
• Visual Odometry for Precise Mobile Robot Positioning
• Facial Recognition System for Human-Robot Interaction
• 3D Object Reconstruction from 2D Images for Robotic Mapping
• Autonomous Drone with Advanced Vision-Based Obstacle Avoidance
• Development of a Visual SLAM System for Autonomous Indoor navigation.

Human-Robot Collaboration

• Development of Robot Assistants for Elderly Care and Companionship
• Implementation of Collaborative Robots (Cobots) in Manufacturing Processes
• Shared Control Interfaces for Teleoperation of Remote Robots
• Ethics and Social Impact Assessment of Human-Robot Interaction
• Evaluation of User Interfaces for Robotic Assistants in Healthcare
• Human-Centric Design of Robotic Exoskeletons for Enhanced Mobility
• Enhancing Worker Safety in Industrial Settings through Human-Robot Collaboration
• Haptic Feedback Systems for Improved Teleoperation of Remote Robots
• Investigating Human Trust and Acceptance of Autonomous Robots in Daily Life
• Design and Testing of Safe and Efficient Human-Robot Collaborative Workstations

Bio-Inspired Robotics

• Biohybrid Robots Combining Biological and Artificial Components for Unique Functions
• Evolutionary Robotics Algorithms for Robot Behavior Optimization
• Swarm Robotics Inspired by Insect Behavior for Collective Tasks
• Design and Fabrication of Soft Robotics for Flexible and Adaptive Movement
• Biomimetic Robotic Fish for Underwater Exploration
• Biorobotics Research for Prosthetic Limb Design and Control
• Development of a Robotic Pollination System Inspired by Bees
• Bio-Inspired Flying Robots for Agile and Efficient Aerial Navigation
• Bio-Inspired Sensing and Localization Techniques for Robotic Applications
• Development of a Legged Robot with Biomimetic Locomotion Inspired by Animals

Robot Learning and AI

• Transfer Learning Strategies for Robotic Applications in Varied Environments
• Explainable AI Models for Transparent Robot Decision-Making
• Robot Learning from Demonstration (LfD) for Complex Tasks
• Machine Learning Algorithms for Predictive Maintenance of Industrial Robots
• Neural Network-Based Vision System for Autonomous Robot Learning
• Reinforcement Learning for UAV Swarms and Cooperative Flight
• Human-Robot Interaction Studies to Improve Robot Learning
• Natural Language Processing for Human-Robot Communication
• Robotic Systems with Advanced AI for Autonomous Exploration
• Implementation of Reinforcement Learning Algorithms for Robotic Control

Robotics in Healthcare

• Design and Testing of Robotic Prosthetics and Exoskeletons for Enhanced Mobility
• Telemedicine Platform for Remote Robotic Medical Consultations
• Robot-Assisted Rehabilitation System for Physical Therapy
• Simulation-Based Training Environment for Robotic Surgical Skill Assessment
• Humanoid Robot for Social and Emotional Support in Healthcare Settings
• Autonomous Medication Dispensing Robot for Hospitals and Pharmacies
• Wearable Health Monitoring Device with AI Analysis
• Robotic Systems for Elderly Care and Fall Detection
• Surgical Training Simulator with Realistic Haptic Feedback
• Development of a Robotic Surgical Assistant for Minimally Invasive Procedures

Robots in Industry

• Quality Control and Inspection Automation with Robotic Systems
• Risk Assessment and Safety Measures for Industrial Robot Environments
• Human-Robot Collaboration Solutions for Manufacturing and Assembly
• Warehouse Automation with Robotic Pick-and-Place Systems
• Industrial Robot Vision Systems for Quality Assurance
• Integration of Cobots in Flexible Manufacturing Cells
• Robot Grippers and End-Effector Design for Specific Industrial Tasks
• Predictive Maintenance Strategies for Industrial Robot Fleet
• Robotics for Lean Manufacturing and Continuous Improvement
• Robotic Automation in Manufacturing: Process Optimization and Integration

Robots in Space Exploration

• Precise Autonomous Spacecraft Navigation for Deep Space Missions
• Robotics for Satellite Servicing and Space Debris Removal
• Lunar and Martian Surface Exploration with Autonomous Robots
• Resource Utilization and Mining on Extraterrestrial Bodies with Robots
• Design and Testing of Autonomous Space Probes for Interstellar Missions
• Autonomous Space Telescopes for Astronomical Observations
• Robot-Assisted Lunar Base Construction and Maintenance
• Planetary Sample Collection and Return Missions with Robotic Systems
• Biomechanics and Human Factors Research for Astronaut-Robot Collaboration
• Autonomous Planetary Rovers: Mobility and Scientific Exploration

Environmental Robotics

• Environmental Monitoring and Data Collection Using Aerial Drones
• Robotics in Wildlife Conservation: Tracking and Protection of Endangered Species
• Disaster Response Robots: Search, Rescue, and Relief Operations
• Autonomous Agricultural Robots for Sustainable Farming Practices
• Autonomous Forest Fire Detection and Firefighting Robot Systems
• Monitoring and Rehabilitation of Coral Reefs with Robotic Technology
• Air Quality Monitoring and Pollution Detection with Mobile Robot Swarms
• Autonomous River and Marine Cleanup Robots
• Ecological Studies and Environmental Protection with Robotic Instruments
• Development of Underwater Robotic Systems for Ocean Exploration and Monitoring

These project ideas span a wide range of topics within robotics research, offering opportunities for innovation, exploration, and contribution to the field. Researchers, students, and enthusiasts can choose projects that align with their interests and expertise to advance robotics technology and its applications.

Robotics Research Topics for high school students

• Home Robots: Explore how robots can assist in daily tasks at home.
• Medical Robotics: Investigate robots used in surgery and patient care.
• Robotics in Education: Learn about robots teaching coding and science.
• Agricultural Robots: Study robots in farming for planting and monitoring.
• Space Exploration: Discover robots exploring planets and space.
• Environmental Robots: Explore robots in conservation and pollution monitoring.
• Ethical Questions: Discuss the ethical dilemmas in robotics.
• DIY Robot Projects: Build and program robots from scratch.
• Robot Competitions: Participate in exciting robotics competitions.
• Cutting-Edge Innovations: Stay updated on the latest in robotics.

These topics offer exciting opportunities for high school students to delve into robotics research, learning, and creativity.

Easy Robotics Research Topics 

Introduction to robotics.

Explore the basics of robotics, including robot components and their functions.

History of Robotics

Investigate the evolution of robotics from its beginnings to modern applications.

Robotic Sensors

Learn about various sensors used in robots for detecting and measuring data.

Simple Robot Building

Build a basic robot using kits or everyday materials and learn about its components.

Programming a Robot

Experiment with programming languages like Scratch or Blockly to control a robot’s movements.

Robots in Entertainment

Explore how robots are used in the entertainment industry, such as animatronics and robot performers.

Robotics in Toys

Investigate robotic toys and their mechanisms, such as remote-controlled cars and drones.

Robotic Pets

Learn about robotic pets like robot dogs and cats and their interactive features.

Robotics in Science Fiction

Analyze how robots are portrayed in science fiction movies and literature.

Robotic Safety

Explore safety measures and protocols when working with robots to prevent accidents.

These topics provide a gentle introduction to robotics research and are ideal for beginners looking to learn more about this exciting field.

Latest Research Topics in Robotics

The field of robotics is ever-evolving, with a plethora of exciting research topics at the forefront of exploration. Here are some of the latest and most intriguing areas of research in robotics:

Soft Robotics

Soft robots, crafted from flexible materials, can adapt to their surroundings, making them safer for human interaction and ideal for unstructured environments.

Robotic Swarms

Groups of robots working collectively toward a common objective, such as search and rescue missions, disaster relief efforts, and environmental monitoring.

Robotic Exoskeletons

Wearable devices designed to enhance human strength and mobility, offering potential benefits for individuals with disabilities, boosting worker productivity, and aiding soldiers in carrying heavier loads.

Medical Robotics

Robots play a vital role in various medical applications, including surgery, rehabilitation, and drug delivery, enhancing precision, reducing human error, and advancing healthcare practices.

Intelligent Robots

Intelligent robots have the ability to learn and adapt to their surroundings, enabling them to tackle complex tasks and interact naturally with humans.

These are just a glimpse of the thrilling research avenues within robotics. As the field continues to progress, we anticipate witnessing even more groundbreaking advancements and innovations in the years ahead.

What topics are in robotics?

Robotics basics.

Understanding the fundamental concepts of robotics, including robot components, kinematics, and control systems.

Robotics History

Exploring the historical development of robotics and its evolution into a multidisciplinary field.

Robot Sensors

Studying the various sensors used in robots for perception, navigation, and interaction with the environment.

Robot Actuators

Learning about the mechanisms and motors that enable robot movement and manipulation.

Robot Control

Understanding how robots are programmed and controlled, including topics like motion planning and trajectory generation.

Robot Mobility

Examining the different types of robot mobility, such as wheeled, legged, aerial, and underwater robots.

Artificial Intelligence in Robotics

Exploring the role of AI and machine learning in enhancing robot autonomy, decision-making, and adaptability.

Human-Robot Interaction

Investigating how robots can effectively interact with humans, including social and ethical considerations.

Robot Perception

Studying computer vision and other technologies that enable robots to perceive and interpret their surroundings.

Robotic Manipulation

Delving into robot arms, grippers, and manipulation techniques for tasks like object grasping and assembly.

Robot Localization and Mapping

Understanding methods for robot localization (knowing their position) and mapping (creating maps of their environment).

Robotics in Medicine

Exploring the use of robots in surgery, rehabilitation, and medical applications.

Analyzing the role of robots in manufacturing and automation, including industrial robot arms and cobots.

Learning about robots capable of making decisions and navigating autonomously in complex environments.

Robot Ethics

Examining ethical considerations related to robotics, including issues of privacy, safety, and AI ethics.

Exploring robots inspired by nature, such as those mimicking animal locomotion or behavior.

Robotic Applications

Investigating specific applications of robots in fields like agriculture, space exploration, entertainment, and more.

Robotics Research Trends

Staying updated on the latest trends and innovations in the field, such as soft robotics, swarm robotics, and intelligent agents.

These topics represent a broad spectrum of areas within robotics, each offering unique opportunities for research, development, and exploration.

What are your 10 robotics ideas?

Home assistant robot.

Build a robot that can assist with everyday tasks at home, like fetching objects, turning lights on and off, or even helping with cleaning.

Robotics in Agriculture

Create a robot for farming tasks, such as planting seeds, monitoring crop health, or even autonomous weed removal.

Autonomous Delivery Robot

Design a robot capable of delivering packages or groceries autonomously within a neighborhood or urban environment.

Search and Rescue Robot

Develop a robot that can navigate disaster-stricken areas to locate and assist survivors or relay important information to rescuers.

Robot Artist

Build a robot that can create art, whether it’s through painting, drawing, or even sculpture.

Underwater Exploration Robot

Construct a remotely operated vehicle (ROV) for exploring the depths of the ocean and gathering data on marine life and conditions.

Robot for the Elderly

Create a companion robot for the elderly that can provide companionship, reminders for medication, and emergency assistance.

Educational Robot

Design a robot that can teach coding and STEM concepts to children in an engaging and interactive way.

Robotics in Space

Develop a robot designed for space exploration, such as a planetary rover or a robot for asteroid mining.

Design a lifelike robotic pet that can offer companionship and emotional support, especially for those unable to care for a real pet.

These project ideas span various domains within robotics, from practical applications to creative endeavors, offering opportunities for innovation and exploration.

What are the 7 biggest challenges in robotics?

Robot autonomy.

Imagine robots that can think for themselves, make decisions, and navigate complex, ever-changing environments like a seasoned explorer.

Robotic Senses

Picture robots with superhuman perception, able to see, hear, and understand the world around them as well as or even better than humans.

Human-Robot Harmony

Think of robots seamlessly working alongside us, understanding our needs, and being safe, friendly, and helpful companions.

Robotic Hands and Fingers

Envision robots with the dexterity of a skilled surgeon, capable of handling delicate and complex tasks with precision.

Robots on the Move

Imagine robots that can gracefully traverse all kinds of terrain, from busy city streets to rugged mountain paths.

Consider the ethical questions surrounding robots, like privacy, fairness, and the impact on employment, as we strive for responsible and beneficial AI.

Robot Teamwork

Visualize a world where robots from different manufacturers can effortlessly work together, just like a symphony orchestra playing in perfect harmony.

What are the 5 major fields of robotics?

Industrial wizards.

Think of robots working tirelessly on factory floors, welding, assembling, and ensuring top-notch quality in the products we use every day.

Helpful Companions

Imagine robots assisting us in non-industrial settings, from healthcare, where they assist in surgery and rehabilitation, to our homes, where they vacuum our floors and make life a little easier.

Mobile Marvels

Picture robots that can move and navigate on their own, exploring uncharted territories in space, performing search and rescue missions, or even delivering packages to our doorstep.

Human-Like Helpers

Envision robots that resemble humans, not just in appearance but also in their movements and interactions. These are the robots designed to understand and assist us in ways that feel natural.

AI-Powered Partners

Think of robots that aren’t just machines but intelligent partners. They learn from experience, adapt to different situations, and make decisions using cutting-edge artificial intelligence and machine learning.

Let’s wrap up our robotics research topics. As we have seen that there is endless innovation in robotics research topics. That is why there are lots of robotics research topics to explore.

As the technology is innovating everyday and continuously evolving there are lots of new challenges and discoveries are emerging in the world of robotics.

With these robotics research topics you would explore a lot about the future endeavors of robotics.  These topics would also tap on your creativity and embrace your knowledge about robotics. So let’s implement these topics and feel the difference.

Frequently Asked Questions

How can i get involved in robotics research.

To get started in robotics research, you can pursue a degree in robotics, computer science, or a related field. Join robotics clubs, attend conferences, and seek out research opportunities at universities or tech companies.

Are there any ethical concerns in robotics research?

Yes, ethical concerns in robotics research include issues related to job displacement, privacy, and the use of autonomous weapons. Researchers are actively addressing these concerns to ensure responsible development.

What are the career prospects in robotics research?

Robotics research offers a wide range of career opportunities, including robotics engineer, AI specialist, data scientist, and robotics consultant. The field is constantly evolving, creating new job prospects.

How can robotics benefit society?

Robotics can benefit society by improving healthcare, increasing manufacturing efficiency, conserving the environment, and aiding in disaster response. It has the potential to enhance various aspects of our lives.

What is the role of AI in robotics research?

AI plays a crucial role in robotics research by enabling robots to make intelligent decisions, adapt to changing environments, and perform complex tasks. AI and robotics are closely intertwined, driving innovation in both fields.

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Recent Posts

• Open access
• Published: 10 February 2023

Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model

• Darmawansah Darmawansah   ORCID: orcid.org/0000-0002-3464-4598 1 ,
• Gwo-Jen Hwang   ORCID: orcid.org/0000-0001-5155-276X 1 , 3 ,
• Mei-Rong Alice Chen   ORCID: orcid.org/0000-0003-2722-0401 2 &
• Jia-Cing Liang   ORCID: orcid.org/0000-0002-1134-527X 1  

International Journal of STEM Education volume  10 , Article number:  12 ( 2023 ) Cite this article

19k Accesses

20 Citations

5 Altmetric

Metrics details

Fostering students’ competence in applying interdisciplinary knowledge to solve problems has been recognized as an important and challenging issue globally. This is why STEM (Science, Technology, Engineering, Mathematics) education has been emphasized at all levels in schools. Meanwhile, the use of robotics has played an important role in STEM learning design. The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. The review indicated that R-STEM education studies were mostly conducted in the United States and mainly in K-12 schools. Learner and teacher perceptions were the most popular research focus in these studies which applied robots. LEGO was the most used tool to accomplish the learning objectives. In terms of application, Technology (programming) was the predominant robotics-based STEM discipline in the R-STEM studies. Moreover, project-based learning (PBL) was the most frequently employed learning strategy in robotics-related STEM research. In addition, STEM learning and transferable skills were the most popular educational goals when applying robotics. Based on the findings, several implications and recommendations to researchers and practitioners are proposed.

Introduction

Over the past few years, implementation of STEM (Science, Technology, Engineering, and Mathematics) education has received a positive response from researchers and practitioners alike. According to Chesloff ( 2013 ), the winning point of STEM education is its learning process, which validates that students can use their creativity, collaborative skills, and critical thinking skills. Consequently, STEM education promotes a bridge between learning in authentic real-life scenarios (Erdoğan et al., 2016 ; Kelley & Knowles, 2016 ). This is the greatest challenge facing STEM education. The learning experience and real-life situation might be intangible in some areas due to pre- and in-conditioning such as unfamiliarity with STEM content (Moomaw, 2012 ), unstructured learning activities (Sarama & Clements, 2009), and inadequate preparation of STEM curricula (Conde et al., 2021 ).

In response to these issues, the adoption of robotics in STEM education has been encouraged as part of an innovative and methodological approach to learning (Bargagna et al., 2019 ; Ferreira et al., 2018 ; Kennedy et al., 2015 ; Köse et al., 2015 ). Similarly, recent studies have reported that the use of robots in school settings has an impact on student curiosity (Adams et al., 2011 ), arts and craftwork (Sullivan & Bers, 2016 ), and logic (Bers, 2008 ). When robots and educational robotics are considered a core part of STEM education, it offers the possibility to promote STEM disciplines such as engineering concepts or even interdisciplinary practices (Okita, 2014 ). Anwar et. al. ( 2019 ) argued that integration between robots and STEM learning is important to support STEM learners who do not immediately show interest in STEM disciplines. Learner interest can elicit the development of various skills such as computational thinking, creativity and motivation, collaboration and cooperation, problem-solving, and other higher-order thinking skills (Evripidou et al., 2020 ). To some extent, artificial intelligence (AI) has driven the use of robotics and tools, such as their application to designing instructional activities (Hwang et al., 2020 ). The potential for research on robotics in STEM education can be traced by showing the rapid increase in the number of studies over the past few years. The emphasis is on critically reviewing existing research to determine what prior research already tells us about R-STEM education, what it means, and where it can influence future research. Thus, this study aimed to fill the gap by conducting a systematic review to grasp the potential of R-STEM education.

In terms of providing the core concepts of roles and research trends of R-STEM education, this study explored beyond the scope of previous reviews by conducting content analysis to see the whole picture. To address the following questions, this study analyzed published research in the Web of Science database regarding the technology-based learning model (Lin & Hwang, 2019 ):

In terms of research characteristic and features, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

In terms of interaction between participants and robots, what were the participants, roles of the robot, and types of robot in the R-STEM education research?

In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

• Literature review

Previous studies have investigated the role of robotics in R-STEM education from several research foci such as the specific robot users (Atman Uslu et al., 2022 ; Benitti, 2012 ; Jung & Won, 2018 ; Spolaôr & Benitti, 2017 ; van den Berghe et al., 2019 ), the potential value of R-STEM education (Çetin & Demircan, 2020 ; Conde et al., 2021 ; Zhang et al., 2021 ), and the types of robots used in learning practices (Belpaeme et al., 2018 ; Çetin & Demircan, 2020 ; Tselegkaridis & Sapounidis, 2021 ). While their findings provided a dynamic perspective on robotics, they failed to contribute to the core concept of promoting R-STEM education. Those previous reviews did not summarize the exemplary practice of employing robots in STEM education. For instance, Spolaôr and Benitti ( 2017 ) concluded that robots could be an auxiliary tool for learning but did not convey whether the purpose of using robots is essential to enhance learning outcomes. At the same time, it is important to address the use and purpose of robotics in STEM learning, the connections between theoretical pedagogy and STEM practice, and the reasons for the lack of quantitative research in the literature to measure student learning outcomes.

First, Benitti ( 2012 ) reviewed research published between 2000 and 2009. This review study aimed to determine the educational potential of using robots in schools and found that it is feasible to use most robots to support the pedagogical process of learning knowledge and skills related to science and mathematics. Five years later, Spolaôr and Benitti ( 2017 ) investigated the use of robots in higher education by employing the adopted-learning theories that were not covered in their previous review in 2012. The study’s content analysis approach synthesized 15 papers from 2002 to 2015 that used robots to support instruction based on fundamental learning theory. The main finding was that project-based learning (PBL) and experiential learning, or so-called hands-on learning, were considered to be the most used theories. Both theories were found to increase learners’ motivation and foster their skills (Behrens et al., 2010 ; Jou et al., 2010 ). However, the vast majority of discussions of the selected reviews emphasized positive outcomes while overlooking negative or mixed outcomes. Along the same lines, Jung and Won ( 2018 ) also reviewed theoretical approaches to Robotics education in 47 studies from 2006 to 2017. Their focused review of studies suggested that the employment of robots in learning should be shifted from technology to pedagogy. This review paper argued to determine student engagement in robotics education, despite disagreements among pedagogical traits. Although Jung and Won ( 2018 ) provided information of teaching approaches applied in robotics education, they did not offer critical discussion on how those approaches were formed between robots and the teaching disciplines.

On the other hand, Conde et. al. ( 2021 ) identified PBL as the most common learning approach in their study by reviewing 54 papers from 2006 to 2019. Furthermore, the studies by Çetin and Demircan ( 2020 ) and Tselegkaridis and Sapounidis ( 2021 ) focused on the types of robots used in STEM education and reviewed 23 and 17 papers, respectively. Again, these studies touted learning engagement as a positive outcome, and disregarded the different perspectives of robot use in educational settings on students’ academic performance and cognition. More recently, a meta-analysis by Zhang et. al. ( 2021 ) focused on the effects of robotics on students’ computational thinking and their attitudes toward STEM learning. In addition, a systematic review by Atman Uslu et. al. ( 2022 ) examined the use of educational robotics and robots in learning.

So far, the review study conducted by Atman Uslu et. al. ( 2022 ) could be the only study that has attempted to clarify some of the criticisms of using educational robots by reviewing the studies published from 2006 to 2019 in terms of their research issues (e.g., interventions, interactions, and perceptions), theoretical models, and the roles of robots in educational settings. However, they failed to take into account several important features of robots in education research, such as thematic subjects and educational objectives, for instance, whether robot-based learning could enhance students’ competence of constructing new knowledge, or whether robots could bring either a motivational facet or creativity to pedagogy to foster students’ learning outcomes. These are essential in investigating the trends of technology-based learning research as well as the role of technology in education as a review study is aimed to offer a comprehensive discussion which derived from various angles and dimensions. Moreover, the role of robots in STEM education was generally ignored in the previous review studies. Hence, there is still a need for a comprehensive understanding of the role of robotics in STEM education and research trends (e.g., research issues, interaction issues, and application issues) so as to provide researchers and practitioners with valuable references. That is, our study can remedy the shortcomings of previous reviews (Additional file 1 ).

The above comments demonstrate how previous scholars have understood what they call “the effectiveness of robotics in STEM education” in terms of innovative educational tools. In other words, despite their useful findings and ongoing recommendations, there has not been a thorough investigation of how robots are widely used from all angles. Furthermore, the results of existing review studies have been less than comprehensive in terms of the potential role of robotics in R-STEM education after taking into account various potential dimensions based on the technology-based model that we propose in this study.

The studies in this review were selected from the literature on the Web of Science, our sole database due to its rigorous journal research and qualified studies (e.g., Huang et al., 2022 ), discussing the adoption of R-STEM education, and the data collection procedures for this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009 ) as referred to by prior studies (e.g., Chen et al., 2021a , 2021b ; García-Martínez et al., 2020 ). Considering publication quality, previous studies (Fu & Hwang, 2018 ; Martín-Páez et al., 2019 ) suggested using Boolean expressions to search Web of Science databases. The search terms for “robot” are “robot” or “robotics” or “robotics” or “Lego” (Spolaôr & Benitti, 2017 ). According to Martín-Páez et. al. ( 2019 ), expressions for STEM education include “STEM” or “STEM education” or “STEM literacy” or “STEM learning” or “STEM teaching” or “STEM competencies”. These search terms were entered into the WOS database to search only for SSCI papers due to its wide recognition as being high-quality publications in the field of educational technology. As a result, 165 papers were found in the database. The search was then restricted to 2012–2021 as suggested by Hwang and Tsai ( 2011 ). In addition, the number of papers was reduced to 131 by selecting only publications of the “article” type and those written in “English”. Subsequently, we selected the category “education and educational research” which reduced the number to 60 papers. During the coding analysis, the two coders screened out 21 papers unrelated to R-STEM education. The coding result had a Kappa coefficient of 0.8 for both coders (Cohen, 1960 ). After the screening stage, a final total of 39 articles were included in this study, as shown in Fig.  1 . Also, the selected papers are marked with an asterisk in the reference list and are listed in Appendixes 1 and 2 .

PRISMA procedure for the selection process

Theoretical model, data coding, and analysis

This study comprised content analysis using a coding scheme to provide insights into different aspects of the studies in question (Chen et al., 2021a , 2021b ; Martín-Páez et al., 2019 ). The coding scheme adopted the conceptual framework proposed by Lin and Hwang ( 2019 ), comprising “STEM environments”, “learners”, and “robots”, as shown in Fig.  2 . Three issues were identified:

In terms of research issues, five dimensions were included: “location”, “sample size”, “duration of intervention”, (Zhong & Xia, 2020 ) “research methods”, (Johnson & Christensen, 2000 ) and “research foci”. (Hynes et al., 2017 ; Spolaôr & Benitti, 2017 ).

In terms of interaction issues, three dimensions were included: “participants”, (Hwang & Tsai, 2011 ), “roles of the robot”, and “types of robot” (Taylor, 1980 ).

In terms of application, five dimensions were included, namely “dominant STEM disciplines”, “integration of robot and STEM” (Martín‐Páez et al., 2019 ), “contribution to STEM disciplines”, “pedagogical intervention”, (Spolaôr & Benitti, 2017 ) and “educational objectives” (Anwar et al., 2019 ). Table 1 shows the coding items in each dimension of the investigated issues.

Model of R-STEM education theme framework

Figure  3 shows the distribution of the publications selected from 2012 to 2021. The first two publications were found in 2012. From 2014 to 2017, the number of publications steadily increased, with two, three, four, and four publications, respectively. Moreover, R-STEM education has been increasingly discussed within the last 3 years (2018–2020) with six, three, and ten publications, respectively. The global pandemic in the early 2020s could have affected the number of papers published, with only five papers in 2021. This could be due to the fact that most robot-STEM education research is conducted in physical classroom settings.

Number of publications on R-STEM education from 2012 to 2021

Table 2 displays the journals in which the selected papers were published, the number of papers published in each journal, and the journal’s impact factor. It can be concluded that most of the papers on R-STEM education research were published in the Journal of Science Education and Technology , and the International Journal of Technology and Design Education , with six papers, respectively.

Research issues

The geographic distribution of the reviewed studies indicated that more than half of the studies were conducted in the United States (53.8%), while Turkey and China were the location of five and three studies, respectively. Taiwan, Canada, and Italy were indicated to have two studies each. One study each was conducted in Australia, Mexico, and the Netherlands. Figure  4 shows the distribution of the countries where the R-STEM education was conducted.

Locations where the studies were conducted ( N  = 39)

Sample size

Regarding sample size, there were four most common sample sizes for the selected period (2012–2021): greater than 80 people (28.21% or 11 out of 39 studies), between 41 and 60 (25.64% or 10 out of 39 studies), 1 to 20 people (23.08% or 9 out of 39), and between 21 and 40 (20.51% or 8 out of 39 studies). The size of 61 to 80 people (2.56% or 1 out of 39 studies) was the least popular sample size (see Fig.  5 ).

Sample size across the studies ( N  = 39)

Duration of intervention

Regarding the duration of the study (see Fig.  6 ), experiments were mostly conducted for less than or equal to 4 weeks (35.9% or 14 out of 39 studies). This was followed by less than or equal to 8 weeks (25.64% or 10 out of 39 studies), less than or equal to 6 months (20.51% or 8 out 39 studies), less than or equal to 12 months (10.26% or 4 out of 39 studies), while less than or equal to 1 day (7.69% or 3 out of 39 studies) was the least chosen duration.

Duration of interventions across the studies ( N  = 39)

Research methods

Figure  7 demonstrates the trends in research methods from 2012 to 2021. The use of questionnaires or surveys (35.9% or 14 out of 39 studies) and mixed methods research (35.9% or 14 out of 39 studies) outnumbered other methods such as experimental design (25.64% or 10 out of 39 studies) and system development (2.56% or 1 out of 39 studies).

Frequency of each research method used in 2012–2021

Research foci

In these studies, research foci were divided into four aspects: cognition, affective, operational skill, and learning behavior. If the study involved more than one research focus, each issue was coded under each research focus.

In terms of cognitive skills, students’ learning performance was the most frequently measured (15 out of 39 studies). Six studies found that R-STEM education brought a positive result to learning performance. Two studies did not find any significant difference, while five studies showed mixed results or found that it depends. For example, Chang and Chen ( 2020 ) revealed that robots in STEM learning improved students’ cognition such as designing, electronic components, and computer programming.

In terms of affective skills, just over half of the reviewed studies (23 out of 39, 58.97%) addressed the students’ or teachers’ perceptions of employing robots in STEM education, of which 14 studies showed positive perceptions. In contrast, nine studies found mixed results. For instance, Casey et. al. ( 2018 ) determined students’ mixed perceptions of the use of robots in learning coding and programming.

Five studies were identified regarding operational skills by investigating students’ psychomotor aspects such as construction and mechanical elements (Pérez & López, 2019 ; Sullivan & Bers, 2016 ) and building and modeling robots (McDonald & Howell, 2012 ). Three studies found positive results, while two reported mixed results.

In terms of learning behavior, five out of 39 studies measured students’ learning behavior, such as students’ engagement with robots (Ma et al., 2020 ), students’ social behavior while interacting with robots (Konijn & Hoorn, 2020 ), and learner–parent interactions with interactive robots (Phamduy et al., 2017 ). Three studies showed positive results, while two found mixed results or found that it depends (see Table 3 ).

Interaction issues

Participants.

Regarding the educational level of the participants, elementary school students (33.33% or 13 studies) were the most preferred study participants, followed by high school students (15.38% or 6 studies). The data were similar for preschool, junior high school, in-service teachers, and non-designated personnel (10.26% or 4 studies). College students, including pre-service teachers, were the least preferred study participants. Interestingly, some studies involved study participants from more than one educational level. For example, Ucgul and Cagiltay ( 2014 ) conducted experiments with elementary and middle school students, while Chapman et. al. ( 2020 ) investigated the effectiveness of robots with elementary, middle, and high school students. One study exclusively investigated gifted and talented students without reporting their levels of education (Sen et al., 2021 ). Figure  8 shows the frequency of study participants between 2012 and 2021.

Frequency of research participants in the selected period

The roles of robot

For the function of robots in STEM education, as shown in Fig.  9 , more than half of the selected articles used robots as tools (31 out of 39 studies, 79.49%) for which the robots were designed to foster students’ programming ability. For instance, Barker et. al. ( 2014 ) investigated students’ building and programming of robots in hands-on STEM activities. Seven out of 39 studies used robots as tutees (17.95%), with the aim of students and teachers learning to program. For example, Phamduy et. al. ( 2017 ) investigated a robotic fish exhibit to analyze visitors’ experience of controlling and interacting with the robot. The least frequent role was tutor (2.56%), with only one study which programmed the robot to act as tutor or teacher for students (see Fig.  9 ).

Frequency of roles of robots

Types of robot

Furthermore, in terms of the types of robots used in STEM education, the LEGO MINDSTORMS robot was the most used (35.89% or 14 out of 39 studies), while Arduino was the second most used (12.82% or 5 out of 39 studies), and iRobot Create (5.12% or 2 out of 39 studies), and NAO (5.12% or 2 out of 39 studies) ranked third equal, as shown in Fig.  10 . LEGO was used to solve STEM problem-solving tasks such as building bridges (Convertini, 2021 ), robots (Chiang et al., 2020 ), and challenge-specific game boards (Leonard et al., 2018 ). Furthermore, four out of 36 studies did not specify the robots used in their studies.

Frequency of types of robots used

Application issues

The dominant disciplines and the contribution to stem disciplines.

As shown in Table 4 , the most dominant discipline in R-STEM education research published from 2012 to 2021 was technology. Engineering, mathematics, and science were the least dominant disciplines. Programming was the most common subject for robotics contribution to the STEM disciplines (25 out of 36 studies, 64.1%), followed by engineering (12.82%), and mathematical method (12.82%). We found that interdisciplinary was discussed in the selected period, but in relatively small numbers. However, this finding is relevant to expose the use of robotics in STEM disciplines as a whole. For example, Barker et. al. ( 2014 ) studied how robotics instructional modules in geospatial and programming domains could be impacted by fidelity adherence and exposure to the modules. The dominance of STEM subjects based on robotics makes it necessary to study the way robotics and STEM are integrated into the learning process. Therefore, the forms of STEM integration are discussed in the following sub-section to report how teaching and learning of these disciplines can have learning goals in an integrated STEM environment.

Integration of robots and STEM

There are three general forms of STEM integration (see Fig.  11 ). Of these studies, robot-STEM content integration was commonly used (22 studies, 56.41%), in which robot activities had multiple STEM disciplinary learning objectives. For example, Chang and Chen ( 2020 ) employed Arduino in a robotics sailboat curriculum. This curriculum was a cross-disciplinary integration, the objectives of which were understanding sailboats and sensors (Science), the direction of motors and mechanical structures (Engineering), and control programming (Technology). The second most common form was supporting robot-STEM content integration (12 out of 39 studies, 30.76%). For instance, KIBO robots were used in the robotics activities where the mechanical elements content area was meaningfully covered in support of the main programming learning objectives (Sullivan & Bers, 2019 ). The least common form was robot-STEM context integration (5 out of 39 studies, 12.82%) which was implemented through the robot to situate the disciplinary content goals in another discipline’s practices. For example, Christensen et. al. ( 2015 ) analyzed the impact of an after-school program that offered robots as part of students’ challenges in a STEM competition environment (geoscience and programming).

The forms of robot-STEM integration

Pedagogical interventions

In terms of instructional interventions, as shown in Fig.  12 , project-based learning (PBL) was the preferred instructional theory for using robots in R-STEM education (38.46% or 15 out 39 studies), with the aim of motivating students or robot users in the STEM learning activities. For example, Pérez and López ( 2019 ) argued that using low-cost robots in the teaching process increased students’ motivation and interest in STEM areas. Problem-based learning was the second most used intervention in this dimension (17.95% or 7 out of 39 studies). It aimed to improve students’ motivation by giving them an early insight into practical Engineering and Technology. For example, Gomoll et. al. ( 2017 ) employed robots to connect students from two different areas to work collaboratively. Their study showed the importance of robotic engagement in preliminary learning activities. Edutainment (12.82% or 5 out of 39 studies) was the third most used intervention. This intervention was used to bring together students and robots and to promote learning by doing. Christensen et. al. ( 2015 ) and Phamduy et. al. ( 2017 ) were the sample studies that found the benefits of hands-on and active learning engagement; for example, robotics competitions and robotics exhibitions could help retain a positive interest in STEM activities.

The pedagogical interventions in R-STEM education

Educational objectives

As far as the educational objectives of robots are concerned (see Fig.  13 ), the majority of robots are used for learning and transfer skills (58.97% or 23 out of 39 studies) to enhance students’ construction of new knowledge. It emphasized the process of learning through inquiry, exploration, and making cognitive associations with prior knowledge. Chang and Chen’s ( 2020 ) is a sample study on how learning objectives promote students’ ability to transfer science and engineering knowledge learned through science experiments to design a robotics sailboat that could navigate automatically as a novel setting. Moreover, it also explicitly aimed to examine the hands-on learning experience with robots. For example, McDonald and Howell ( 2012 ) described how robots engaged with early year students to better understand the concepts of literacy and numeracy.

Educational objectives of R-STEM education

Creativity and motivation were found to be educational objectives in R-STEM education for seven out of 39 studies (17.94%). It was considered from either the motivational facet of social trend or creativity in pedagogy to improve students’ interest in STEM disciplines. For instance, these studies were driven by the idea that employing robots could develop students’ scientific creativity (Guven et al., 2020 ), confidence and presentation ability (Chiang et al., 2020 ), passion for college and STEM fields (Meyers et al., 2012 ), and career choice (Ayar, 2015 ).

The general benefits of educational robots and the professional development of teachers were equally found in four studies each. The first objective, the general benefits of educational robotics, was to address those studies that found a broad benefit of using robots in STEM education without highlighting the particular focus. The sample studies suggested that robotics in STEM could promote active learning and improve students’ learning experience through social interaction (Hennessy Elliott, 2020 ) and collaborative science projects (Li et al., 2016 ). The latter, teachers’ professional development, was addressed by four studies (10.25%) to utilize robots to enhance teachers’ efficacy. Studies in this category discussed how teachers could examine and identify distinctive instructional approaches with robotics work (Bernstein et al., 2022 ), design meaningful learning instruction (Ryan et al., 2017 ) and lesson materials (Kim et al., 2015 ), and develop more robust cultural responsive self-efficacy (Leonard et al., 2018 ).

This review study was conducted using content analysis from the WOS collection of research on robotics in STEM education from 2012 to 2021. The findings are discussed under the headings of each research question.

RQ 1: In terms of research, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

About half of the studies were conducted in North America (the USA and Canada), while limited studies were found from other continents (Europe and the Asia Pacific). This trend was identified in the previous study on robotics for STEM activities (Conde et al., 2021 ). Among 39 studies, 28 (71.79%) had fewer than 80 participants, while 11 (28.21%) had more than 80 participants. The intervention’s duration across the studies was almost equally divided between less than or equal to a month (17 out of 39 studies, 43.59%) and more than a month (22 out of 39 studies, 56.41%). The rationale behind the most popular durations is that these studies were conducted in classroom experiments and as conditional learning. For example, Kim et. al. ( 2018 ) conducted their experiments in a course offered at a university where it took 3 weeks based on a robotics module.

A total of four different research methodologies were adopted in the studies, the two most popular being mixed methods (35.89%) and questionnaires or surveys (35.89%). Although mixed methods can be daunting and time-consuming to conduct (Kucuk et al., 2013 ), the analysis found that it was one of the most used methods in the published articles, regardless of year. Chang and Chen ( 2022 ) embedded a mixed-methods design in their study to qualitatively answer their second research question. The possible reason for this is that other researchers prefer to use mixed methods as their research design. Their main research question was answered quantitatively, while the second and remaining research questions were reported through qualitative analysis (Casey et al., 2018 ; Chapman et al., 2020 ; Ma et al., 2020 ; Newton et al., 2020 ; Sullivan & Bers, 2019 ). Thus, it was concluded that mixed methods could lead to the best understanding and integration of research questions (Creswell & Clark, 2013 ; Creswell et al., 2003 ).

In contrast, system development was the least used compared to other study designs, as most studies used existing robotic systems. It should be acknowledged that the most common outcome we found was to enable students to understand these concepts as they relate to STEM subjects. Despite the focus on system development, the help of robotics was identified as increasing the success of STEM learning (Benitti, 2012 ). Because limited studies focused on system development as their primary purpose (1 out of 39 studies, 2.56%), needs analyses may ask whether the mechanisms, types, and challenges of robotics are appropriate for learners. Future research will need further design and development of personalized robots to fill this part of the research gap.

About half of the studies (23 studies, 58.97%) were focused on investigating the effectiveness of robots in STEM learning, primarily by collecting students’ and teachers’ opinions. This result is more similar to Belpaeme et al. ( 2018 ) finding that users’ perceptions were common measures in studies on robotics learning. However, identifying perceptions of R-STEM education may not help us understand exactly how robots’ specific features afford STEM learning. Therefore, it is argued that researchers should move beyond such simple collective perceptions in future research. Instead, further studies may compare different robots and their features. For instance, whether robots with multiple sensors, a sensor, or without a sensor could affect students’ cognitive, metacognitive, emotional, and motivational in STEM areas (e.g., Castro et al., 2018 ). Also, there could be instructional strategies embedded in R-STEM education that can lead students to do high-order thinking, such as problem-solving with a decision (Özüorçun & Bicen, 2017 ), self-regulated and self-engagement learning (e.g., Li et al., 2016 ). Researchers may also compare the robotics-based approach with other technology-based approaches (e.g., Han et al., 2015 ; Hsiao et al., 2015 ) in supporting STEM learning.

RQ 2: In terms of interaction, what were the participants, roles of the robots, and types of robots of the R-STEM education research?

The majority of reviewed studies on R-STEM education were conducted with K-12 students (27 studies, 69.23%), including preschool, elementary school, junior, and high school students. There were limited studies that involved higher education students and teachers. This finding is similar to the previous review study (Atman Uslu et al., 2022 ), which found a wide gap among research participants between K-12 students and higher education students, including teachers. Although it is unclear why there were limited studies conducted involving teachers and higher education students, which include pre-service teachers, we are aware of the critical task of designing meaningful R-STEM learning experiences which is likely to require professional development. In this case, both pre- and in-service teachers could examine specific objectives, identify topics, test the application, and design potential instruction to align well with robots in STEM learning (Bernstein et al., 2022 ). Concurrently, these pedagogical content skills in R-STEM disciplines might not be taught in the traditional pre-service teacher education and particular teachers’ development program (Huang et al., 2022 ). Thus, it is recommended that future studies could be conducted to understand whether robots can improve STEM education for higher education students and teachers professionally.

Regarding the role of robots, most were used as learning tools (31 studies, 79.48%). These robots are designed to have the functional ability to command or program some analysis and processing (Taylor, 1980 ). For example, Leonard et. al. ( 2018 ) described how pre-service teachers are trained in robotics activities to facilitate students’ learning of computational thinking. Therefore, robots primarily provide opportunities for learners to construct knowledge and skills. Only one study (2.56%), however, was found to program robots to act as tutors or teachers for students. Designing a robot-assisted system has become common in other fields such as language learning (e.g., Hong et al., 2016 ; Iio et al., 2019 ) and special education (e.g., Özdemir & Karaman, 2017 ) where the robots instruct the learning activities for students. In contrast, R-STEM education has not looked at the robot as a tutor, but has instead focused on learning how to build robots (Konijn & Hoorn, 2020 ). It is argued that robots with features as human tutors, such as providing personalized guidance and feedback, could assist during problem-solving activities (Fournier-Viger et al., 2013 ). Thus, it is worth exploring in what teaching roles the robot will work best as a tutor in STEM education.

When it comes to types of robots, the review found that LEGO dominated robots’ employment in STEM education (15 studies, 38.46%), while the other types were limited in their use. It is considered that LEGO tasks are more often associated with STEM because learners can be more involved in the engineering or technical tasks. Most researchers prefer to use LEGO in their studies (Convertini, 2021 ). Another interesting finding is about the cost of the robots. Although robots are generally inexpensive, some products are particularly low-cost and are commonly available in some regions (Conde et al., 2021 ). Most preferred robots are still considered exclusive learning tools in developing countries and regions. In this case, only one study offered a low-cost robot (Pérez & López, 2019 ). This might be a reason why the selected studies were primarily conducted in the countries and continents where the use of advanced technologies, such as robots, is growing rapidly (see Fig.  4 ). Based on this finding, there is a need for more research on the use of low-cost robots in R-STEM instruction in the least developed areas or regions of the world. For example, Nel et. al. ( 2017 ) designed a STEM program to build and design a robot which exclusively enabling students from low-income household to participate in the R-STEM activities.

RQ 3: In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

While Technology and Engineering are the dominant disciplines, this review found several studies that directed their research to interdisciplinary issues. The essence of STEM lies in interdisciplinary issues that integrate one discipline into another to create authentic learning (Hansen, 2014 ). This means that some researchers are keen to develop students’ integrated knowledge of Science, Technology, Engineering, and Mathematics (Chang & Chen, 2022 ; Luo et al., 2019 ). However, Science and Mathematics were given less weight in STEM learning activities compared to Technology and Engineering. This issue has been frequently reported as a barrier to implementing R-STEM in the interdisciplinary subject. Some reasons include difficulties in pedagogy and classroom roles, lack of curriculum integration, and a limited opportunity to embody one learning subject into others (Margot & Kettler, 2019 ). Therefore, further research is encouraged to treat these disciplines equally, so is the way of STEM learning integration.

The subject-matter results revealed that “programming” was the most common research focus in R-STEM research (25 studies). Researchers considered programming because this particular topic was frequently emphasized in their studies (Chang & Chen, 2020 , 2022 ; Newton et al., 2020 ). Similarly, programming concepts were taught through support robots for kindergarteners (Sullivan & Bers, 2019 ), girls attending summer camps (Chapman et al., 2020 ), and young learners with disabilities (Lamptey et al., 2021 ). Because programming simultaneously accompanies students’ STEM learning, we believe future research can incorporate a more dynamic and comprehensive learning focus. Robotics-based STEM education research is expected to encounter many interdisciplinary learning issues.

Researchers in the reviewed studies agreed that the robot could be integrated with STEM learning with various integration forms. Bryan et. al. ( 2015 ) argued that robots were designed to develop multiple learning goals from STEM knowledge, beginning with an initial learning context. It is parallel with our finding that robot-STEM content integration was the most common integration form (22 studies, 56.41%). In this form, studies mainly defined their primary learning goals with one or more anchor STEM disciplines (e.g., Castro et al., 2018 ; Chang & Chen, 2020 ; Luo et al., 2019 ). The learning goals provided coherence between instructional activities and assessments that explicitly focused on the connection among STEM disciplines. As a result, students can develop a deep and transferable understanding of interdisciplinary phenomena and problems through emphasizing the content across disciplines (Bryan et al., 2015 ). However, the findings on learning instruction and evaluation in this integration are inconclusive. A better understanding of the embodiment of learning contexts is needed, for instance, whether instructions are inclusive, socially relevant, and authentic in the situated context. Thus, future research is needed to identify the quality of instruction and evaluation and the specific characteristics of robot-STEM integration. This may place better provision of opportunities for understanding the form of pedagogical content knowledge to enhance practitioners’ self-efficacy and pedagogical beliefs (Chen et al., 2021a , 2021b ).

Project-based learning (PBL) was the most used instructional intervention with robots in R-STEM education (15 studies, 38.46%). Blumenfeld et al. ( 1991 ) credited PBL with the main purpose of engaging students in investigating learning models. In the case of robotics, students can create robotic artifacts (Spolaôr & Benitti, 2017 ). McDonald and Howell ( 2012 ) used robotics to develop technological skills in lower grades. Leonard et. al. ( 2016 ) used robots to engage and develop students’ computational thinking strategies in another example. In the aforementioned study, robots were used to support learning content in informal education, and both teachers and students designed robotics experiences aligned with the curriculum (Bernstein et al., 2022 ). As previously mentioned, this study is an example of how robots can cover STEM content from the learning domain to support educational goals.

The educational goal of R-STEM education was the last finding of our study. Most of the reviewed studies focused on learning and transferable skills as their goals (23 studies, 58.97%). They targeted learning because the authors investigated the effectiveness of R-STEM learning activities (Castro et al., 2018 ; Convertini, 2021 ; Konijn & Hoorn, 2020 ; Ma et al., 2020 ) and conceptual knowledge of STEM disciplines (Barak & Assal, 2018 ; Gomoll et al., 2017 ; Jaipal-Jamani & Angeli 2017 ). They targeted transferable skills because they require learners to develop individual competencies in STEM skills (Kim et al., 2018 ; McDonald & Howell, 2012 ; Sullivan & Bers, 2016 ) and to master STEM in actual competition-related skills (Chiang et al., 2020 ; Hennessy Elliott, 2020 ).

Conclusions and implications

The majority of the articles examined in this study referred to theoretical frameworks or certain applications of pedagogical theories. This finding contradicts Atman Uslu et. al. ( 2022 ), who concluded that most of the studies in this domain did not refer to pedagogical approaches. Although we claim the employment pedagogical frameworks in the examined articles exist, those articles primarily did not consider a strict instructional design when employing robots in STEM learning. Consequently, the discussions in the studies did not include how the learning–teaching process affords students’ positive perceptions. Therefore, both practitioners and researchers should consider designing learning instruction using robots in STEM education. To put an example, the practitioners may regard students’ zone of proximal development (ZPD) when employing robot in STEM tasks. Giving an appropriate scaffolding and learning contents are necessary for them to enhance their operational skills, application knowledge and emotional development. Although the integration between robots and STEM education was founded in the reviewed studies, it is worth further investigating the disciplines in which STEM activities have been conducted. This current review found that technology and engineering were the subject areas of most concern to researchers, while science and mathematics did not attract as much attention. This situation can be interpreted as an inadequate evaluation of R-STEM education. In other words, although those studies aimed at the interdisciplinary subject, most assessments and evaluations were monodisciplinary and targeted only knowledge. Therefore, it is necessary to carry out further studies in these insufficient subject areas to measure and answer the potential of robots in every STEM field and its integration. Moreover, the broadly consistent reporting of robotics generally supporting STEM content could impact practitioners only to employ robots in the mainstream STEM educational environment. Until that point, very few studies had investigated the prominence use of robots in various and large-scale multidiscipline studies (e.g., Christensen et al., 2015 ).

Another finding of the reviewed studies was the characteristic of robot-STEM integration. Researchers and practitioners must first answer why and how integrated R-STEM could be embodied in the teaching–learning process. For example, when robots are used as a learning tool to achieve STEM learning objectives, practitioners are suggested to have application knowledge. At the same time, researchers are advised to understand the pedagogical theories so that R-STEM integration can be flexibly merged into learning content. This means that the learning design should offer students’ existing knowledge of the immersive experience in dealing with robots and STEM activities that assist them in being aware of their ideas, then building their knowledge. In such a learning experience, students will understand the concept of STEM more deeply by engaging with robots. Moreover, demonstration of R-STEM learning is not only about the coherent understanding of the content knowledge. Practitioners need to apply both flexible subject-matter knowledge (e.g., central facts, concepts and procedures in the core concept of knowledge), and pedagogical content knowledge, which specific knowledge of approaches that are suitable for organizing and delivering topic-specific content, to the discipline of R-STEM education. Consequently, practitioners are required to understand the nature of robots and STEM through the content and practices, for example, taking the lead in implementing innovation through subject area instruction, developing collaboration that enriches R-STEM learning experiences for students, and being reflective practitioners by using students’ learning artifacts to inform and revise practices.

Limitations and recommendations for future research

Overall, future research could explore the great potential of using robots in education to build students’ knowledge and skills when pursuing learning objectives. It is believed that the findings from this study will provide insightful information for future research.

The articles reviewed in this study were limited to journals indexed in the WOS database and R-STEM education-related SSCI articles. However, other databases and indexes (e.g., SCOPUS, and SCI) could be considered. In addition, the number of studies analyzed was relatively small. Further research is recommended to extend the review duration to cover the publications in the coming years. The results of this review study have provided directions for the research area of STEM education and robotics. Specifically, robotics combined with STEM education activities should aim to foster the development of creativity. Future research may aim to develop skills in specific areas such as robotics STEM education combined with the humanities, but also skills in other humanities disciplines across learning activities, social/interactive skills, and general guidelines for learners at different educational levels. Educators can design career readiness activities to help learners build self-directed learning plans.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Science, technology, engineering, and mathematics

Robotics-based STEM

Project-based learning

References marked with an asterisk indicate studies included in the systematic review

Adams, R., Evangelou, D., English, L., De Figueiredo, A. D., Mousoulides, N., Pawley, A. L., Schiefellite, C., Stevens, R., Svinicki, M., Trenor, J. M., & Wilson, D. M. (2011). Multiple perspectives on engaging future engineers. Journal of Engineering Education, 100 (1), 48–88. https://doi.org/10.1002/j.2168-9830.2011.tb00004.x

Article   Google Scholar  

Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A systematic review of studies on educational robotics. Journal of Pre-College Engineering Education Research (j-PEER), 9 (2), 19–24. https://doi.org/10.7771/2157-9288.1223

Atman Uslu, N., Yavuz, G. Ö., & KoçakUsluel, Y. (2022). A systematic review study on educational robotics and robots. Interactive Learning Environments . https://doi.org/10.1080/10494820.2021.2023890

*Ayar, M. C. (2015). First-hand experience with engineering design and career interest in engineering: An informal STEM education case study. Educational Sciences Theory & Practice, 15 (6), 1655–1675. https://doi.org/10.12738/estp.2015.6.0134

*Barak, M., & Assal, M. (2018). Robotics and STEM learning: Students’ achievements in assignments according to the P3 Task Taxonomy—Practice, problem solving, and projects. International Journal of Technology and Design Education, 28 (1), 121–144. https://doi.org/10.1007/s10798-016-9385-9

Bargagna, S., Castro, E., Cecchi, F., Cioni, G., Dario, P., Dell’Omo, M., Di Lieto, M. C., Inguaggiato, E., Martinelli, A., Pecini, C., & Sgandurra, G. (2019). Educational robotics in down syndrome: A feasibility study. Technology, Knowledge and Learning, 24 (2), 315–323. https://doi.org/10.1007/s10758-018-9366-z

*Barker, B. S., Nugent, G., & Grandgenett, N. F. (2014). Examining fidelity of program implementation in a STEM-oriented out-of-school setting. International Journal of Technology and Design Education, 24 (1), 39–52. https://doi.org/10.1007/s10798-013-9245-9

Behrens, A., Atorf, L., Schwann, R., Neumann, B., Schnitzler, R., Balle, J., Herold, T., Telle, A., Noll, T. G., Hameyer, K., & Aach, T. (2010). MATLAB meets LEGO Mindstorms—A freshman introduction course into practical engineering. IEEE Transactions on Education, 53 (2), 306–317. https://doi.org/10.1109/TE.2009.2017272

Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3 (21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954

Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58 (3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006

*Bernstein, D., Mutch-Jones, K., Cassidy, M., & Hamner, E. (2022). Teaching with robotics: Creating and implementing integrated units in middle school subjects. Journal of Research on Technology in Education . https://doi.org/10.1080/15391523.2020.1816864

Bers, M. U. (2008). Blocks to robots learning with technology in the early childhood classroom . Teachers College Press.

Google Scholar  

Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: sustaining the doing, supporting the learning. Educational Psychologist, 26 (3–4), 369–398. https://doi.org/10.1080/00461520.1991.9653139

Bryan, L. A., Moore, T. J., Johnson, C. C., & Roehrig, G. H. (2015). Integrated STEM education. In C. C. Johnson, E. E. Peters-Burton, & T. J. Moore (Eds.), STEM road map: A framework for integrated STEM education (pp. 23–37). Routledge.

Chapter   Google Scholar  

*Casey, J. E., Gill, P., Pennington, L., & Mireles, S. V. (2018). Lines, roamers, and squares: Oh my! using floor robots to enhance Hispanic students’ understanding of programming. Education and Information Technologies, 23 (4), 1531–1546. https://doi.org/10.1007/s10639-017-9677-z

*Castro, E., Cecchi, F., Valente, M., Buselli, E., Salvini, P., & Dario, P. (2018). Can educational robotics introduce young children to robotics and how can we measure it?. Journal of Computer Assisted Learning, 34 (6), 970–977. https://doi.org/10.1111/jcal.12304

Çetin, M., & Demircan, H. Ö. (2020). Empowering technology and engineering for STEM education through programming robots: A systematic literature review. Early Child Development and Care, 190 (9), 1323–1335. https://doi.org/10.1080/03004430.2018.1534844

*Chang, C. C., & Chen, Y. (2020). Cognition, attitude, and interest in cross-disciplinary i-STEM robotics curriculum developed by thematic integration approaches of webbed and threaded models: A concurrent embedded mixed methods study. Journal of Science Education and Technology, 29 , 622–634. https://doi.org/10.1007/s10956-020-09841-9

*Chang, C. C., & Chen, Y. (2022). Using mastery learning theory to develop task-centered hands-on STEM learning of Arduino-based educational robotics: Psychomotor performance and perception by a convergent parallel mixed method. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1741400

*Chapman, A., Rodriguez, F. D., Pena, C., Hinojosa, E., Morales, L., Del Bosque, V., Tijerina, Y., & Tarawneh, C. (2020). “Nothing is impossible”: Characteristics of Hispanic females participating in an informal STEM setting. Cultural Studies of Science Education, 15 , 723–737. https://doi.org/10.1007/s11422-019-09947-6

Chen, M. R. A., Hwang, G. J., Majumdar, R., Toyokawa, Y., & Ogata, H. (2021a). Research trends in the use of E-books in English as a foreign language (EFL) education from 2011 to 2020: A bibliometric and content analysis. Interactive Learning Environments . https://doi.org/10.1080/10494820.2021.1888755

Chen, Y. L., Huang, L. F., & Wu, P. C. (2021b). Preservice preschool teachers’ self-efficacy in and need for STEM education professional development: STEM pedagogical belief as a mediator. Early Childhood Education Journal, 49 (2), 137–147.

Chesloff, J. D. (2013). STEM education must start in early childhood. Education Week, 32 (23), 27–32.

*Chiang, F. K., Liu, Y. Q., Feng, X., Zhuang, Y., & Sun, Y. (2020). Effects of the world robot Olympiad on the students who participate: A qualitative study. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1775097

*Christensen, R., Knezek, G., & Tyler-Wood, T. (2015). Alignment of hands-on STEM engagement activities with positive STEM dispositions in secondary school students. Journal of Science Education and Technology, 24 (6), 898–909. https://doi.org/10.1007/s10956-015-9572-6

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20 , 37–46. https://doi.org/10.1177/001316446002000104

Conde, M. Á., Rodríguez-Sedano, F. J., Fernández-Llamas, C., Gonçalves, J., Lima, J., & García-Peñalvo, F. J. (2021). Fostering STEAM through challenge-based learning, robotics, and physical devices: A systematic mapping literature review. Computer Applications in Engineering Education, 29 (1), 46–65. https://doi.org/10.1002/cae.22354

*Convertini, J. (2021). An interdisciplinary approach to investigate preschool children’s implicit inferential reasoning in scientific activities. Research in Science Education, 51 (1), 171–186. https://doi.org/10.1007/s11165-020-09957-3

Creswell, J. W., & Clark, V. L. P. (2013). Designing and conducting mixed methods research (3rd ed.). Thousand Oaks: Sage Publications Inc.

Creswell, J. W., Plano-Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. Handbook of mixed methods in social and behavioral research. Sage.

Erdoğan, N., Navruz, B., Younes, R., & Capraro, R. M. (2016). Viewing how STEM project-based learning influences students’ science achievement through the implementation lens: A latent growth modeling. EURASIA Journal of Mathematics, Science & Technology Education, 12 (8), 2139–2154. https://doi.org/10.12973/eurasia.2016.1294a

Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A. A., Zinonos, Z., & Chatzichristofis, S. A. (2020). Educational robotics: Platforms, competitions and expected learning outcomes. IEEE Access, 8 , 219534–219562. https://doi.org/10.1109/ACCESS.2020.3042555

Ferreira, N. F., Araujo, A., Couceiro, M. S., & Portugal, D. (2018). Intensive summer course in robotics–Robotcraft. Applied Computing and Informatics, 16 (1/2), 155–179. https://doi.org/10.1016/j.aci.2018.04.005

Fournier-Viger, P., Nkambou, R., Nguifo, E. M., Mayers, A., & Faghihi, U. (2013). A multiparadigm intelligent tutoring system for robotic arm training. IEEE Transactions on Learning Technologies, 6 (4), 364–377. https://doi.org/10.1109/TLT.2013.27

Fu, Q. K., & Hwang, G. J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119 , 129–143. https://doi.org/10.1016/j.compedu.2018.01.004

García-Martínez, I., Tadeu, P., Montenegro-Rueda, M., & Fernández-Batanero, J. M. (2020). Networking for online teacher collaboration. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1764057

*Gomoll, A., Hmelo-Silver, C. E., Šabanović, S., & Francisco, M. (2016). Dragons, ladybugs, and softballs: Girls’ STEM engagement with human-centered robotics. Journal of Science Education and Technology, 25 (6), 899–914. https://doi.org/10.1007/s10956-016-9647-z

*Gomoll, A. S., Hmelo-Silver, C. E., Tolar, E., Šabanovic, S., & Francisco, M. (2017). Moving apart and coming together: Discourse, engagement, and deep learning. Educational Technology and Society, 20 (4), 219–232.

*Guven, G., KozcuCakir, N., Sulun, Y., Cetin, G., & Guven, E. (2020). Arduino-assisted robotics coding applications integrated into the 5E learning model in science teaching. Journal of Research on Technology in Education . https://doi.org/10.1080/15391523.2020.1812136

Han, J., Jo, M., Hyun, E., & So, H. J. (2015). Examining young children’s perception toward augmented reality-infused dramatic play. Educational Technology Research and Development, 63 (3), 455–474. https://doi.org/10.1007/s11423-015-9374-9

Hansen, M. (2014). Characteristics of schools successful in STEM: Evidence from two states’ longitudinal data. Journal of Educational Research, 107 (5), 374–391. https://doi.org/10.1080/00220671.2013.823364

*Hennessy Elliott, C. (2020). “Run it through me:” Positioning, power, and learning on a high school robotics team. Journal of the Learning Sciences, 29 (4–5), 598–641. https://doi.org/10.1080/10508406.2020.1770763

Hong, Z. W., Huang, Y. M., Hsu, M., & Shen, W. W. (2016). Authoring robot-assisted instructional materials for improving learning performance and motivation in EFL classrooms. Journal of Educational Technology & Society, 19 (1), 337–349.

Hsiao, H. S., Chang, C. S., Lin, C. Y., & Hsu, H. L. (2015). “iRobiQ”: The influence of bidirectional interaction on kindergarteners’ reading motivation, literacy, and behavior. Interactive Learning Environments, 23 (3), 269–292. https://doi.org/10.1080/10494820.2012.745435

Huang, B., Jong, M. S. Y., Tu, Y. F., Hwang, G. J., Chai, C. S., & Jiang, M. Y. C. (2022). Trends and exemplary practices of STEM teacher professional development programs in K-12 contexts: A systematic review of empirical studies. Computers & Education . https://doi.org/10.1016/j.compedu.2022.104577

Hwang, G. J., & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42 (4), E65–E70.

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1 , 100001. https://doi.org/10.1016/j.caeai.2020.100001

Hynes, M. M., Mathis, C., Purzer, S., Rynearson, A., & Siverling, E. (2017). Systematic review of research in P-12 engineering education from 2000–2015. International Journal of Engineering Education, 33 (1), 453–462.

Iio, T., Maeda, R., Ogawa, K., Yoshikawa, Y., Ishiguro, H., Suzuki, K., Aoki, T., Maesaki, M., & Hama, M. (2019). Improvement of Japanese adults’ English speaking skills via experiences speaking to a robot. Journal of Computer Assisted Learning, 35 (2), 228–245. https://doi.org/10.1111/jcal.12325

*Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary preservice teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26 (2), 175–192. https://doi.org/10.1007/s10956-016-9663-z

Johnson, B., & Christensen, L. (2000). Educational research: Quantitative and qualitative approaches . Allyn & Bacon.

Jou, M., Chuang, C. P., & Wu, Y. S. (2010). Creating interactive web-based environments to scaffold creative reasoning and meaningful learning: From physics to products. Turkish Online Journal of Educational Technology-TOJET, 9 (4), 49–57.

Jung, S., & Won, E. (2018). Systematic review of research trends in robotics education for young children. Sustainability, 10 (4), 905. https://doi.org/10.3390/su10040905

Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3 , 11. https://doi.org/10.1186/s40594-016-0046-z

Kennedy, J., Baxter, P., & Belpaeme, T. (2015). Comparing robot embodiments in a guided discovery learning interaction with children. International Journal of Social Robotics, 7 (2), 293–308. https://doi.org/10.1007/s12369-014-0277-4

*Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers and Education., 91 , 14–31. https://doi.org/10.1016/j.compedu.2015.08.005

*Kim, C. M., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46 (5), 767–787. https://doi.org/10.1007/s11251-018-9453-5

*Konijn, E. A., & Hoorn, J. F. (2020). Robot tutor and pupils’ educational ability: Teaching the times tables. Computers and Education, 157 , 103970. https://doi.org/10.1016/j.compedu.2020.103970

Köse, H., Uluer, P., Akalın, N., Yorgancı, R., Özkul, A., & Ince, G. (2015). The effect of embodiment in sign language tutoring with assistive humanoid robots. International Journal of Social Robotics, 7 (4), 537–548. https://doi.org/10.1007/s12369-015-0311-1

Kucuk, S., Aydemir, M., Yildirim, G., Arpacik, O., & Goktas, Y. (2013). Educational technology research trends in Turkey from 1990 to 2011. Computers & Education, 68 , 42–50. https://doi.org/10.1016/j.compedu.2013.04.016

Lamptey, D. L., Cagliostro, E., Srikanthan, D., Hong, S., Dief, S., & Lindsay, S. (2021). Assessing the impact of an adapted robotics programme on interest in science, technology, engineering and mathematics (STEM) among children with disabilities. International Journal of Disability, Development and Education, 68 (1), 62–77. https://doi.org/10.1080/1034912X.2019.1650902

*Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25 (6), 860–876. https://doi.org/10.1007/s10956-016-9628-2

*Leonard, J., Mitchell, M., Barnes-Johnson, J., Unertl, A., Outka-Hill, J., Robinson, R., & Hester-Croff, C. (2018). Preparing teachers to engage rural students in computational thinking through robotics, game design, and culturally responsive teaching. Journal of Teacher Education, 69 (4), 386–407. https://doi.org/10.1177/0022487117732317

*Li, Y., Huang, Z., Jiang, M., & Chang, T. W. (2016). The effect on pupils’ science performance and problem-solving ability through Lego: An engineering design-based modeling approach. Educational Technology and Society, 19 (3), 143–156. https://doi.org/10.2307/jeductechsoci.19.3.14

Lin, H. C., & Hwang, G. J. (2019). Research trends of flipped classroom studies for medical courses: A review of journal publications from 2008 to 2017 based on the technology-enhanced learning model. Interactive Learning Environments, 27 (8), 1011–1027. https://doi.org/10.1080/10494820.2018.1467462

*Luo, W., Wei, H. R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM survey to evaluate a middle school robotics learning environment: Validity evidence in a different context. Journal of Science Education and Technology, 28 (4), 429–443. https://doi.org/10.1007/s10956-019-09773-z

*Ma, H. L., Wang, X. H., Zhao, M., Wang, L., Wang, M. R., & Li, X. J. (2020). Impact of robotic instruction with a novel inquiry framework on primary schools students. International Journal of Engineering Education, 36 (5), 1472–1479.

Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A systematic literature review. International Journal of STEM Education, 6 (1), 1–16. https://doi.org/10.1186/s40594-018-0151-2

Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education, 103 (4), 799–822. https://doi.org/10.1002/sce.21522

*McDonald, S., & Howell, J. (2012). Watching, creating and achieving: Creative technologies as a conduit for learning in the early years. British Journal of Educational Technology, 43 (4), 641–651. https://doi.org/10.1111/j.1467-8535.2011.01231.x

*Meyers, K., Goodrich, V. E., Brockman, J. B., & Caponigro, J. (2012). I2D2: Imagination, innovation, discovery, and design. In 2012 ASEE annual conference & exposition (pp. 25–707). https://doi.org/10.18260/1-2--21464

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine . https://doi.org/10.1371/journal.pmed.1000097

Moomaw, S. (2012). STEM Begins in the Early Years. School Science and Mathematics, 112 (2), 57–58. https://doi.org/10.1111/j.1949-8594.2011.00119.x

Nel, H., Ettershank, M., & Venter, J. (2017). AfrikaBot: Design of a robotics challenge to promote STEM in Africa. In M. Auer, D. Guralnick, & J. Uhomoibhi (Eds.), Interactive collaborative learning. ICL 2016. Advances in intelligent systems and computing. Springer. https://doi.org/10.1007/978-3-319-50340-0_44

*Newton, K. J., Leonard, J., Buss, A., Wright, C. G., & Barnes-Johnson, J. (2020). Informal STEM: Learning with robotics and game design in an urban context. Journal of Research on Technology in Education, 52 (2), 129–147. https://doi.org/10.1080/15391523.2020.1713263

Okita, S. Y. (2014). The relative merits of transparency: Investigating situations that support the use of robotics in developing student learning adaptability across virtual and physical computing platforms. British Journal of Educational Technology, 45 (5), 844–862. https://doi.org/10.1111/bjet.12101

Özdemir, D., & Karaman, S. (2017). Investigating interactions between students with mild mental retardation and humanoid robot in terms of feedback types. Education and Science, 42 (191), 109–138. https://doi.org/10.15390/EB.2017.6948

Özüorçun, N. Ç., & Bicen, H. (2017). Does the inclusion of robots affect engineering students’ achievement in computer programming courses? Eurasia Journal of Mathematics, Science and Technology Education, 13 (8), 4779–4787. https://doi.org/10.12973/eurasia.2017.00964a

*Pérez, S. E., & López, J. F. (2019). An ultra-low cost line follower robot as educational tool for teaching programming and circuit’s foundations. Computer Applications in Engineering Education, 27 (2), 288–302. https://doi.org/10.1002/cae.22074

*Phamduy, P., Leou, M., Milne, C., & Porfiri, M. (2017). An interactive robotic fish exhibit for designed settings in informal science learning. IEEE Transactions on Education, 60 (4), 273–280. https://doi.org/10.1109/TE.2017.2695173

*Ryan, M., Gale, J., & Usselman, M. (2017). Integrating engineering into core science instruction: Translating NGSS principles into practice through iterative curriculum design. International Journal of Engineering Education., 33 (1), 321–331.

*Sen, C., Ay, Z. S., & Kiray, S. A. (2021). Computational thinking skills of gifted and talented students in integrated STEM activities based on the engineering design process: The case of robotics and 3D robot modeling. Thinking Skills and Creativity, 42 , 100931. https://doi.org/10.1016/j.tsc.2021.100931

Spolaôr, N., & Benitti, F. B. V. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education, 112 , 97–107. https://doi.org/10.1016/j.compedu.2017.05.001

*Stewart, W. H., Baek, Y., Kwid, G., & Taylor, K. (2021). Exploring factors that influence computational thinking skills in elementary students’ collaborative robotics. Journal of Educational Computing Research, 59 (6), 1208–1239. https://doi.org/10.1177/0735633121992479

*Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26 (1), 3–20. https://doi.org/10.1007/s10798-015-9304-5

*Sullivan, A., & Bers, M. U. (2019). Investigating the use of robotics to increase girls’ interest in engineering during early elementary school. International Journal of Technology and Design Education, 29 , 1033–1051. https://doi.org/10.1007/s10798-018-9483-y

*Taylor, M. S. (2018). Computer programming with pre-K through first-grade students with intellectual disabilities. Journal of Special Education, 52 (2), 78–88. https://doi.org/10.1177/0022466918761120

Taylor, R. P. (1980). Introduction. In R. P. Taylor (Ed.), The computer in school: Tutor, tool, tutee (pp. 1–10). Teachers College Press.

Tselegkaridis, S., & Sapounidis, T. (2021). Simulators in educational robotics: A review. Education Sciences, 11 (1), 11. https://doi.org/10.3390/educsci11010011

*Üçgül, M., & Altıok, S. (2022). You are an astroneer: The effects of robotics camps on secondary school students’ perceptions and attitudes towards STEM. International Journal of Technology and Design Education, 32 (3), 1679–1699. https://doi.org/10.1007/s10798-021-09673-7

*Ucgul, M., & Cagiltay, K. (2014). Design and development issues for educational robotics training camps. International Journal of Technology and Design Education, 24 (2), 203–222. https://doi.org/10.1007/s10798-013-9253-9

van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., Van der Ven, S., & Leseman, P. (2019). Social robots for language learning: A review. Review of Educational Research, 89 (2), 259–295. https://doi.org/10.3102/0034654318821286

Zhang, Y., Luo, R., Zhu, Y., & Yin, Y. (2021). Educational robots improve K-12 students’ computational thinking and STEM attitudes: Systematic review. Journal of Educational Computing Research, 59 (7), 1450–1481. https://doi.org/10.1177/0735633121994070

Zhong, B., & Xia, L. (2020). A systematic review on exploring the potential of educational robotics in mathematics education. International Journal of Science and Mathematics Education, 18 (1), 79–101. https://doi.org/10.1007/s10763-018-09939-y

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Acknowledgements

The authors would like to express their gratefulness to the three anonymous reviewers for providing their precious comments to refine this manuscript.

This study was supported by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3; National Science and Technology Council (TW) (NSTC 111-2410-H-031-092-MY2); Soochow University (TW) (111160605-0014). Any opinions, findings, conclusions, and/or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of Ministry of Science and Technology of Taiwan.

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DD, MR and GJ conceptualized the study. MR wrote the outline and DD wrote draft. DD, MR and GJ contributed to the manuscript through critical reviews. DD, MR and GJH revised the manuscript. DD, MR and GJ finalized the manuscript. DD edited the manuscript. MR and GJ monitored the project and provided adequate supervision. DD, MR and JC contributed with data collection, coding, analyses and interpretation. All authors read and approved the final manuscript.

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Darmawansah, D., Hwang, GJ., Chen, MR.A. et al. Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. IJ STEM Ed 10 , 12 (2023). https://doi.org/10.1186/s40594-023-00400-3

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Characteristics of student engagement in high-school robotics courses

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Student engagement has been described as active involvement in a learning activity that significantly affects learning achievement. This study investigated student engagement in robotics education, considering it as an instant emotional reaction on interaction with the teacher, the peers, and the robotic environment. The objective was to characterize engagement in high school robotics courses through the lenses of preparation for academic and technical careers. Students who participated in this study (N = 41), all of whom were in the eleventh grade, belonged to either School A (n 1  = 20) or School B (n 2  = 21). School A students studied only one subject at an advanced level—mechatronics, while each student in School B studied engineering systems as well as one of the following three subjects at an advanced level: computer science, a natural science subject, or mathematics. Data were collected via structured classroom observations, interviews, and a questionnaire. From the analysis of the collected data, we identified 23 engagement structures in total, 12 of which were already known in the literature, and 11 of which were novel. The two groups of students shared nine known structures, and no novel structures. Unlike previous studies of engagement structures, this study was based on an entire year of observations. Additionally, it is one of the first studies of high school student engagement in robotics education. Our findings and conclusions contribute to understanding of student engagement in robotic education, allowing robotics teachers to tailor their instruction more effectively.

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Introduction

In recent years, demand for robots has been rising, driven by new developments in automation and computing. This rise in demand has been accompanied by a growing prevalence of robotics education in schools (Anwar et al., 2019 ). Robotics is considered as an avenue through which students can actively engage in learning concepts from different disciplines (Church et al., 2010 ; Cross et al., 2015 ; Korchnoy & Verner, 2010 ; Verner & Hershko, 2003 ). More than that, the students can develop the skills of adaptability, self-regulation, learning in digital technology environments, and other skills (Ananiadou & Claro, 2009 ; Benitti, 2012 ; National Research Council, 2012 ) that are vitally needed for learning in the era of the COVID-19 pandemic.

The term student engagement has been used to describe the level of active involvement of students in their learning tasks and activities. Engagement largely determines student–teacher interactions, class atmosphere, and learning outcomes. Research indicates that engagement is positively linked with student’s prior academic achievement (Dong et al., 2020 ; Lee, 2014 ; Lei et al., 2018 ; Piñeiro et al., 2019 ). Fortunately, there are many pathways into robotics that can engage students of different interests and backgrounds (Alimisis, 2013 ; Rusk et al., 2008 ). Researchers have observed students performing robotics tasks dedicating extra time to solving robotics problems, as students were trying to find solutions by themselves (You et al., 2006 ).

Need and rationale for study

Technology is an optional subject in Israel. About one-third of Israeli high school students choose to study technology subjects. Out of those students, about one-third of those students choose the track that prepares them for academic engineering programs, another third—the track preparing them for technical college, and the remaining third—the track preparing them for working life. The first track is usually open only to students who excel academically, while the latter two tracks do not require high levels of academic achievement (Małgorzata et al., 2018 ).

Robotics educators adapt the subject for school students of different academic achievement levels. In educating high achieving students, educators emphasise career readiness and twenty-first century skills, while for lower achieving students, robotics serves a tool for experiential learning of STEM concepts (Spolaôr & Benitti, 2017 ; Verner & Revzin, 2017 ) and for strengthening self-efficacy beliefs (Barak, 2010 ).

The potential of robotic learning environments for motivating students has been recognized in the literature (e.g., Brown et al., 2013 ; Hashimoto et al., 2013 ). However, student engagement in distinct relation to the learning pathways described above has not been investigated. Unlike the present study, our previous studies of student engagement in robotics education did not include multiple long-term observations of student behaviour (Verner, 2013 ; Verner & Revzin, 2017 ). These studies began with the analysis of high achieving middle school students’ engagement in an outreach robotics course (Verner, 2013 ). Then, in the study of learning engagement of low achieving high school students majoring in technology education, we came up with an idea to apply the theory of engagement structures.

The construct of engagement structures was introduced by Goldin et al. ( 2011 ) to describe common types of engagement which are triggered by the instant motivational desires of students engaged in learning mathematics. Since its introduction, the construct of engagement structures has been investigated in several studies of learning engagement (e.g., Craft & Capraro, 2017 ; Khalil et al., 2019 ; Perez & Verner, 2019 ; Verner & Revzin, 2017 ; Verner et al., 2013 ).

Research objective and research questions

The objective of this study was to characterize engagement in high school robotics courses through the lenses of preparation for academic and technical careers. The research questions were:

What engagement structures can be identified in high school students studying in a robotics course?

What are the commonalities and differences between engagement structures of the two groups of students—those preparing for academic engineering programs and those preparing for technical college?

The rest of this manuscript is structured as follows: first, an overview of the literature on student engagement, learning theories and instructional methods in robotics education is provided. Next, the methods of this study are detailed and explained, followed by its findings. The last is the discussion, where the key findings are elaborated on, the limitations and contribution of the study are described, the implications of this study for robotics education are outlined, and recommendations for educators and for researchers are provided.

Student engagement and robotics education

This section discusses selected literature on student engagement, engagement structures, the impact of engagement on academic achievement, and student engagement in robotic environments.

• Student engagement

The subject of student engagement has been widely discussed in literature and conceptualized from many different perspectives (Trowler, 2010 ). In this paper we will mention only those aspects of the subject that are most relevant for our study. Newman defined student engagement as “the student’s psychological investment in and effort directed toward learning, understanding, or mastering the knowledge, skills, or crafts that academic work is intended to promote” (Newmann, 1992 , p. 12). Behavioural, cognitive, and affective engagement of students in their learning activities largely determines learning outcomes, student–teacher interactions, and class atmosphere (Lei et al., 2018 ). Student engagement cannot be separated or disentangled from the social context in which it occurs; it is a joint product of his or her motivation and classroom support or thwarting (Reeve, 2012 ).

• Engagement structures

Goldin et al. ( 2011 ) focused on the study of student engagement that can be referred to as an instant emotional reaction on interaction with the teacher, the peers, or the learning environment. The construct of an engagement structure, introduced by them, is intended to describe specific types of student engagement that are typically observed in different categories of students and certain learning situations. The authors proposed to identify and determine engagement structures by inductive analysis of student behaviours repeatedly observed during the learning activities.

The construct of an engagement structure includes the following components: the statement of the motivating desire, the scheme of the social interaction, the characteristics of situations which are likely to evoke the desire, and the behavioural patterns being exhibited. The statement of the motivating desire is formulated so, as to represent a certain typical kind of motivating desires that can be observed in different categories of students and in different subjects. The other components of the engagement structure represent motivating desires that are typically observed in a specific category of students learning a specific subject.

Goldin et al. used engagement structures to characterise student engagement in urban middle school mathematics classrooms. Furthermore, the authors proposed a general approach on how to investigate engagement and called to identify and determine engagement structures in different learning activities. Verner et al. ( 2013 ) applied the construct of engagement structures to the study of engagement in an ethno-mathematically based teacher education course. They observed some of the engagement structures described by Goldin et al. ( 2011 ) and identified a new structure related to cultural identity of students. Verner and Revzin ( 2017 ) applied the theory for the study of engagement in an automated chemistry laboratory course. The study indicated the presence of some of the engagement structures found by Goldin et al. and discovered three new structures specific to the category of learners and type of learning environment.

The engagement structures found in the studies mentioned above are summarized in Table 1 .

Engagement and achievement

Engagement has been positively linked to student academic achievement (Lee, 2014 ). For example, Wu and Huang ( 2007 ) investigated 9th-grade cognitive, emotional, and behavioural engagement in teacher-centred and student-centred technology-enhanced classrooms. The authors reported that low-achieving students in the student-centred group learning demonstrated disengaged behaviours and participated in few conceptual discussions. This low-level engagement seemed to have a negative impact on their learning achievements. The findings suggest that there is no universal instructional approach suitable for engaging every student.

Previous studies have found that lower achieving students tend to exhibit less engagement with learning than higher achieving students (Jensen 2013 ) and that student perception of the school environment affects their academic motivation and engagement (Alonso-Nuez et al., 2020 ). Students on vocational tracks tend to be lower achievers than students who excel academically (Kelly & Price, 2009 ).

Self-determination theory assumes that all students possess inherent growth needs that provide a motivational foundation for their high-quality classroom engagement and positive school functioning. This theory emphasizes the instructional task of vitalizing students’ inner motivational resources as the key step in facilitating high-quality engagement. It identifies the inner motivational resources that all students possess, and offers recommendations as to how teachers can mobilize, nurture, and vitalize these resources during the instruction to facilitate high-quality student engagement (Reeve, 2012 ).

Learning engagement in robotic environments

One of the most important duties of a teacher is to offer students opportunities for hands-on exploration and to provide tools to construct knowledge (Alimisis, 2013 ). This knowledge construction can happen most effectively in a context where the student is engaged in constructing a technological artefact (Papert, 1980 ). The wide range of robotics applications can engage students with different interests and from various backgrounds (Alimisis, 2013 ; Rusk et al., 2008 ). One of the reasons behind the apparent improvements in student engagement when learning with robots is that robots are especially appealing to students due to their physical form and novelty (Kaburlasos & Vrochidou, 2019 ).

Learning with robots can benefit students of different academic levels. Collaborative learning in robotic environments can benefit high achieving students by developing their teamwork skills, which are essential for the current workforce (Ananiadou & Claro, 2009 ; Korchnoy & Verner, 2010 ; National Research Council, 2012 ; Verner & Hershko, 2003 ). For lower achieving students the hands-on interdisciplinary activities with robots provide an effective way to learn STEM concepts and acquire technical skills (Cuperman & Verner, 2013 ; Spolaôr & Benitti, 2017 ).

Brown et al. ( 2013 ) applied behavioural strategies for engagement using robotic educational agents. In the study, the authors focused on a testing scenario to evaluate the role and efficacy of engagement using a robotic agent. In order to enable human interaction, the robot was programmed with a range of verbal and nonverbal behaviours. The authors reported that the approach effectively eliminates idle time and keeps the student engaged in the allocated tasks.

Verner and Revzin ( 2017 ) proposed an educational approach in which high school students majoring in mechanical engineering will be involved in the development of robotic laboratory devices and use them for chemical experiments. The authors observed learning activities at different stages of the course and characterized students’ engagement structures throughout these stages. They concluded that engagement structures can evolve and do not have to remain fixed within students.

In this section we explain the research methodology and describe setting of the study and participant sample. Next, we give a detailed description of the robotics courses followed up in this study, the instruments of data collection, and the analyses of collected data. Finally, we outline how ethical standards were upheld for the study.

Research methodology

Technology education is a broad field which includes various disciplines and engages different categories of students. This high level of variance presents challenges for studying learner engagement from a general perspective, even in just one discipline such as robotics education. Taking this breadth and diversity into consideration, the research methodology chosen for this study can be best described as multiple case studies, in which data about a highly context-dependent phenomenon is collected from multiple cases (Creswell et al., 2007 ). This study investigates two groups of students in the high school mechanical engineering track: one group from a program meant to prepare students for technical careers, and another group in a program meant to prepare students for academic studies in engineering. Thusly, the present study covers two ends of the spectrum in high school robotics education, providing a broader perspective of the phenomenon under investigation than in the case of a single case study.

In Israel robotics is taught in schools in different ways, making valid quantitative comparisons between different schools difficult. Even in one robotics class, the diversity of experiential activities and the difficulty to quantitatively analyse students’ engagement led us to opt for using a qualitative approach. More specifically, the researchers employed engagement structures as a theoretical framework and applied a theory-driven, top-down approach (Braun and Clarke, 2006 ) for analysing the collected data. Previous empirical studies on engagement structures have also opted for a qualitative approach (e.g., Verner et al., 2013 ).

Research setting

The study took place at two locations in Haifa, a city in the north of Israel. The first location was in the Technology Laboratory of the University Faculty of Education in Science and Technology. The second location of the study was at an urban comprehensive private school. In each location, a different group of high school students was taught a robotics course by their respective teacher. The curricula were different, but both ground on design and inquiry activities with robots.

School A is an urban public school. Due to a recent change of location and some funding challenges, it had no laboratory facilities. The University afforded the school with the opportunity to conduct the robotics course at the Technology Laboratory of the University.

School B is an urban private comprehensive school. Recently, the school opened an elective program for high school technology in robotics and engineering systems. Lessons in this program are conducted in the school’s own technology laboratory and two classrooms.

Research sample

Students who participated in this study (N = 41) belonged to one of two schools: School A or School B. School A students (n 1  = 20) were all 11th graders—12 girls and eight boys, studying robotics as part of high school mechatronics track, with the specific aim of providing students with the necessary background in science and technology required for technician studies following their high school graduation. most of the students in the School A group were not accepted into more prestigious high school tracks, such as computer science or natural sciences. The teacher of this class was a veteran physics teacher and Ph.D. holder with research and practical experience in teaching students who are not high achievers in science and technology. He was tasked by the school to design and teach a robotics course specifically for the students in question.

School B students (n 2  = 21) were all 11th graders—19 boys and two girls. All of them were matriculating in computer science, a natural science subject, or mathematics. Recently, the school established an elective robotics and engineering systems program for high school students, with the specific aim of preparing students for academic studies in engineering. School B students who participated in this study were taught by an engineering teacher with extensive professional experience in the hi-tech industry.

The courses

While both courses—in School A and in School B—centred on experiential learning, design and inquiry in robotic environments, the overall aim of each course was different: in the case of the course taught to School A’s students, the overall aim was to teach them basic scientific concepts through experimentation with robots, while in the case of the course taught to School B, the overall aim was to teach students how to solve real-life problems by designing and building robots. The students in School B had already learned the basics of robotics in the 10th grade, and therefore did not need to learn this as the students in School A did.

The objectives of the course were to (1) know the principles of motor operation, mechanical transmissions, sensors, and open- and closed-loop control, (2) understand the scientific and mathematical concepts involved in the design and operation of robotic models, (3) acquire basic skills in design and analysis of robotic models, and (4) acquire basic skills of scientific experimentation. Learning activities revolved around several assignments that the students performed individually or in pairs. In addition, the students executed three robotics projects. These projects tied back to the overall aim of the course, which was to teach students basic scientific concepts through robots.

The course progression was as follows: (1) presenting a physical concept to students, (2) guiding students in planning of experiment, (3) teaching students the relevant concepts and analysis tools, (4) guiding students in conducting experiment and collecting data, (5) guiding students in analyzing collected data, (6) guiding students in preparing the assignment report, and (7) guiding students in presentation of the assignment.

When performing the assignments, the students used different technological tools. They constructed robots from the Lego Mindstorms EV3 kits and 3D printed the needed additional parts using the MakerBot Replicator (Fig.  1 A). The students programmed robots with MindStorms software. In physical experiments, they collected and processed data from robot sensors by means of Fourier’s Data Logger (Fig.  1 B).

Students from School A at work: A Robot construction; B Making an experiment

The objectives of the School B group course were to (1) understand the principles of robot motion and control, (2) acquire basic skills of robot construction, programming, and operation, (3) acquire basic skills of 3D design and printing, (4) develop applied problem-solving skills.

The course progression was as follows: (1) learning fundamental concepts in robotics and applying them, (2) designing and implementing a given electronics-based project, (3) designing and implementing a robotics solution to a challenge posed by a commercial company, and (4) redesigning part of the classroom to introduce a robotics component, e.g., a smart screen. Throughout the semester, students were introduced to technological innovations and were taught ad-hoc topics that the teacher deemed as required for strengthening their proficiency as designers and implementors of robotic projects.

The students used a wide set of technological tools. They constructed robots from the Lego Mindstorms EV3 kits. The Arduino Uno and Raspberry Pi controllers were used for programming robot behaviours, remote control, and statistical analysis. The students designed the needed additional parts with SolidWorks and produced them in plastic using TierTime 3D printer or in wood and metal using CNC, grinding, and drilling machines. Figure  2 shows images of students while carrying out project activities.

School B students at work on robot projects: A Design; B Construction; C Guidance

Data collection and analysis

Data collection was carried out over the entire school year, resulting in frequent, long-term observations of student behaviours. Data were collected by the second author, who acted as an observer in the classrooms. Importantly, he was not involved in the design or teaching of any of the course curricula.

For each of the two student groups, data were collected thusly: throughout each lesson, indications of engagement in robotics activities were identified and recorded in the observation protocol. This protocol was developed by two of the co-authors based on Hatch ( 2002 ). The observation protocol form included the following data: date, participating students, lesson topic, lesson objective, project assignment, lesson assignments, learned concepts, problems solved by the students, teacher's interaction with the students, notable episodes of student engagement. At the end of each lesson, the in-class observer discussed the observations made during that lesson with the teacher, thus supplementing them with the teacher’s interpretation of the collected data. At the end of the course, a questionnaire was deployed among individual students, followed by individual student interviews, both intended for verifying and supplementing previously collected data.

Data analysis was carried out in the following way. We selected from among the noticed episodes of student engagement those pertaining to each engagement structure known from the literature and used the episodes to characterize the structure in the considered context. Then, we analysed the episodes that did not match the known engagement structures. We selected episodes that reflected a certain common pattern of learning engagement and formulated an engagement structure that expressed the pattern. In formulating engagement structures, we followed the recommendations of Goldin et al. ( 2011 ).

The researchers involved the respective teacher of each participant group in the analysis. The teacher’s point of view is naturally important, since they are familiar with students’ behaviour and can also provide context for it based on their familiarity with their students.

Based on the observations recorded by the in-class observer, the authors held regular meetings in which they discussed episodes of the learning process that provided evidence of engagement or disengagement were discerned, and subsequently analysed those episodes to determine patterns of learning engagement. For each pattern, they first examined whether it fits one of the engagement structures already described in the literature (see Table 1 ); in cases where they could not identify any such structure, they tried to ascertain whether a novel engagement structure can be described. The discussion with the teacher at the end of each lesson concerned the patterns of engagement observed by both the teacher and researcher, with the teacher often providing the interviewer with explanations pertaining to student intentions and possible reasons behind their behaviour.

The engagement structures identified using the observation protocol and teacher interviews were verified and supplemented using a questionnaire (see Appendix), which was deployed to individual students following the end of the course, after the final course grade was provided to all the students. The questionnaire included open-ended questions intended to gather data on engagement structures already known and found in the existing literature, whether these were already identified using the previous tools or not. Each item in the questionnaire pertained to one distinct engagement structure.

Finally, the engagement structures that were identified using the previously collected data, whether already known or newly discovered, were verified and supplemented with further data concerning internal factors of engagement—such as desires and beliefs—by conducting an open-ended interview with each student. Green et al., ( 2012 ) noted that the advantage of an open-ended interview is that it allows extending and clarifying the informant’s responses through probing.

Study ethics

Ethical clearance and permissions for the study were obtained from the University Behavioural Sciences Research Ethics Committee and from the Ministry of Education Office of the Chief Scientist. The goal and method of our study was explained to the participating students in advance, and they each were told explicitly that they had the right to cease their participation and exit the study with no explanation or ramifications. Participants' identities were kept anonymous. In-class observations were made with the consent of the schools’ teachers and with the permission of the schools’ respective principals. Participating students and their legal guardians signed A letter of consent to participate in the research. Due to Ministry of Education guidelines for research studies, we were not able to obtain students’ grades.

This section gives an account of the engagement structures which the researchers had identified in students, based on the collected and analysed data. The section is divided into three sub-sections: first, we discuss and illustrate by examples the engagement structures that are described in literature and identified in our study; second, we present novel engagement structures not mentioned in literature (to the best of the authors’ knowledge); and third, we compare the engagement structures identified in the two groups to indicate the differences in their learning engagement.

Throughout this section, participant codes are used instead of names; each code starts with a letter denoting the group, and a number denoting the student in the list of participants. For example: A03 is a student #3 in the list of students from School A.

Existing engagement structures

The researchers identified 12 engagement structures already described in the literature (see Table 1 ) and indicated in our study. Three out of these structures were observed in one of the groups, with the rest identified in both groups. Due to restrictions by the Israel Ministry of Education, student names could not be taken in the questionnaire, and thus could not be identified.

Table 2 presents these engagement structures with representative quotes from the interviews given by students from the groups where the structures were identifiedNovel engagement structures

The researchers identified 11 engagement structures which they did not find in the literature. Interestingly, not one of these novel structures was common to both groups of students: six novel structures listed in Table 3 were identified in the School A group.

The five novel structures identified in the School B group are presented in Table 4 .

The following are detailed descriptions of the novel engagement structures the researchers had identified in the School A group.

Explain in my native language

As repeatedly observed during the course, the students of the School A group experienced difficulties in understanding the concepts taught by the teacher in Hebrew. They expressed confusion, especially when the teacher used higher vocabulary. When the teacher, wishing to help the students, switched to Russian, they rejoiced and more deeply engaged in learning.

I learn with my body

The researchers observed episodes when the students were especially engaged in learning when it involved bodily experiences to examine physics concepts and ideas. This typically happened at the stage when the students learned concepts and analysed tools to be applied in the robotic assignments. For instance, A17—a female student—and two other students [A16, A18] had difficulties in understanding some physics concepts, so the teacher let the students examine these concepts through bodily experiences. In one of the lessons, A17 and the other two students found it hard to learn the concept of the centre of gravity (COG). The teacher asked the students to sit in a chair with their back against the chair and their feet on the floor. Then he asked the students to try to stand up with their back straight. After the students tried unsuccessfully, he explained why this did not work in terms of COG. The students smiled and expressed that after those experiences, they understood the concept. From there, they were engaged in the assignment for the rest of the lesson.

Based on the interview comments, for these students, it was very important to include this kind of activity in their learning process. The teacher noted that he found the effectiveness of such kind of group activities for these students.

Let me learn on my own

The observations showed that some students were engaged in the learning activities only when they worked alone; for example, A12, a male student who showed low levels of engagement in the beginning of the course. A12’s classmate [A15] distracted him from learning by talking about topics unrelated to the learning activity. In one of the lessons, [A15] was absent, and A12 worked alone. He was observed in this lesson to be closely engaged in the assignment and performed it well. Since then, with the consent of A12, the teacher has let him work alone and perform assignments individually. The teacher commented that he repeatedly observed this learning behaviour. His solution was to reorganize the teams and let such students work alone. In the interview, A12 said that before the interview, he did not pay attention to this fact, but when the researcher asked him about it, he realized that he worked better when he worked alone.

Please teach me

The researchers observed episodes when the students were not confident in performing the assignment and only worked under the teacher’s guidance. For instance, A14 was a female student who wanted neither to work alone nor with a teammate, but only when the teacher tutored her personally. Sometimes, the teacher did not help her much, but his presence made the student feel confident in performing the task. This observation was supported by the comments that the student gave in the interview.

The teacher commented that from his experience, this behaviour was quite typical in the first stage of the course. In his opinion, it is essential to provide such students with individual guidance. Therefore, he recommended involving a second teacher or at least a teacher’s assistant in the class. Even if the group only numbers 12 students, the attention they demand is beyond what can be provided by one teacher.

This is hard for me

Some students claimed that they did not have the background knowledge needed to perform the assignment. For example, in one instance when the teacher explained the task to A11, a female student, she just drew a Cartesian plane in her notebook and claimed that she was unable to perform this task. She said that she lacked the knowledge and skills in mathematics and physics needed for the task. We repeatedly observed this type of behaviour in this student during the course. The teacher's strategy was to strengthen the student's confidence. In the beginning, he gave her simple tasks with full support. Then, each lesson, the teacher gave her less support, increased the complexity level of the tasks, and assigned more responsibility to the student. At the end of the year, the student was able to carry out the project with little support and presented it to the class. It was a significant achievement considering that at the beginning of the course, she claimed she was not even able to construct a simple robotic model.

The teacher noted that nurturing of student's self-confidence is a long-term process. In the above example this process took eleven months for the student to achieve some level of self-confidence and self-dependence.

This is boring

We observed students who refused to train the skill by exercising it. For example, A20 was a male student who complained that the core of the assignments always was the same. The point is that when the teacher asked the student to do a new task using the skills that were acquired through the “boring” activities, he showed a lack of the skills and simply did not know what to do. The teacher’s strategy was to let the student see the value of knowledge and skills learned in the course and the importance of training through exercise and through practical activities. This observation was supported by the comments that A20 gave in the interview, where he reinforced the fact that he did the same and it was boring for him.

The teacher commented that it is typical for some students in this category to claim that they “already know everything,” but in fact, when the teacher asked them to perform a task, sometimes these students did not know what to do. Therefore, in the teacher's opinion, it is crucial to engage such students in experiential learning, which includes training by exercise.

Below are detailed descriptions of the novel engagement structures identified in the School B group.

I’m doing this only while it’s fun

From the observations, there were episodes in which students were engaged in learning assignments when they considered them as play activities. For instance, B07 was a male student who perceived the project as a game to be played. He became interested when the task was to build something for the robotic model. At some point, he stopped working and became disengaged with the learning activity. He explained to the researcher that he enjoyed the activity but did not want to spend the entire lesson on it, as it would not be fun. He did not show interest in programming and theoretical lessons, but he liked the electronics activities. He said that in other subjects like physics, he just solved problems and learned theoretical topics; on the other hand, when learning engineering systems, he built interesting things and applied everything he saw in the physics class. So, he enjoyed the lessons, and thought they were fun.

The teacher said that he could not understand this student. The student always said he liked the course, and that he felt excited about the projects. However, the teacher had to attract his attention continually and the student always asked what to do next, even if the teacher already explained the assignment. The teacher commented that on one hand, the student claimed that he was excited, but on the other hand, he always needed someone to push him back to performing the assignment.

I am just an assistant

We observed students who were deeply engaged in the projects but did not performed, by themselves, hands on activities of robot construction and programming. These students only assisted to their teammates. If the teacher required them to program a sensor or develop part of the mechanism in the robotic projects, they didn’t like it and became disengaged. For instance, B04—a male student—avoided getting involved in hands on activities. He liked to support his classmates by painting, writing project-related texts, designing the poster and the slides for the presentation, etc. If peers asked B04 to help in construction or programming, then he agreed and engaged in the activity. It was difficult to engage him in learning in any other way.

The teacher commented that this episode shows his difficulties in teaching the student who did not want to learn robotics but did want to assist his classmates. The teacher also commented that he knew B04's parents and they had pressured B04 to join the course as it gives bonus points to enter the University. Another reason to join the course was that B04’s friends had also taken it and B04 wanted to be with them. The teacher was aware that the student used to do things less related to the project and acted as an assistant without getting involved in the task. He confessed that sometimes allowed B04 to perform activities such as designing a poster, which was not exactly part of the course's curriculum. However, it was the only way the teacher could keep him engaged in the course's activities. In the teacher’s opinion, the student was not happy with the course.

I don’t like being corrected

We identified episodes in which students were engaged in the assignment, but when the teacher pointed out mistakes in their work, the students reacted negatively to the comments, felt offended, and in consequence turned to disengage for a period from one hour up to the rest of the lesson. For example, B06 was a male student who was deeply engaged in the projects. However, when the teacher looked at his work and found errors, the student became frustrated and upset. B06 stopped working on the project and turned to web surfing and talking with peers on topics unconnected to the assignment. He was disengaged for quite a long time and only returned to work on the project at the end of the lesson. Such episodes were repeatedly observed during the course.

The teacher noted that students of this category are accustomed to achieving high grades and receiving praise at school and in their home. Therefore, some of them could feel offended when the teacher pointed out the errors they made. The teacher explained that some of these students felt that teacher’s comments about their mistakes can cast doubt on their status of smart students.

I will make my dreams come true

We observed students who showed deep engagement and concentration on the task and who were driven by the desire to achieve their long-term goals. For example, B16was a male student who showed exceptional engagement and concentration during the projects. In the theoretical part, he asked questions to be sure he understood everything. In the practical lessons, he tried to dedicate extra time to the project and always wanted to go a step further than the rest of the students. He helped students who asked for it but rejected attempts to distract him from the learning. On his own initiative, B16 changed one of his major subjects from computer science to engineering systems. His concentration on and level of immersion in the task were outstanding. He did not express excitement but looked motivated and immersed in the assignments. The level of his learning engagement was higher than that described in literature by the engagement structure “I’m really into this”.

The teacher commented that this student was one of the best students in the group. The teacher knew that the student was good in computer science, and he was honoured to accept him in the group when B16 asked his permission. The teacher said this student was outstanding in his group for his creativity and the level of learning engagement. The teacher also noted the student was very interested in the subject because he decided to join the engineering systems course even though in computer science, he would receive a higher bonus. Moreover, the teacher commented that one of the reasons for this learning behaviour was that his parents instilled him the desire to learn and strongly influenced his excellent behaviour.

In the interview, the student explained that he moved from computer science to engineering systems because in computer science, it was no chance to build something with a 3D printer, actuators, etc. While in engineering systems, he has this chance as well as the opportunity of programming system controllers. Regarding his learning behaviour, B16 explained that he behaved the same way in all the subjects and wanted to be successful in all his learning activities. Also, at home, his parents taught him to respect and value the teacher’s work by respecting the teacher and do his best on each assignment. The students said he wanted to be a pilot in the future.

My way or no way at all

The researchers observed episodes when the students were only engaged when they solved problems and carried out tasks in their own way. If that worked, they continued to be engaged in the task; but if not, they stopped and became disengaged. For instance, B12 was a male student who had worked on a project and who had always dedicated a great amount of time to his assignments. However, the current project had to be developed in his way. If he faced a problem, he tried to find a solution on the Internet. If he could not find the solution, he looked for a peer who could help him. If he did not get help from peers he became disengaged and started to talk with friends and play games on the Internet. Similar cases were found in other students.

The teacher commented that through his experiences he has realized that some of the students are accustomed to certain learning strategies, and their success and failure in applying these strategies largely determines their learning engagement. The teacher noticed that he experienced difficulties in trying to change their learning strategies.

This section begins with a summary of our findings, directly addressing our research questions. We continue by discussing the engagement structures we had identified in relation to existing literature on student engagement, technology education, and learning with robots. We end with implications of our findings to robotics education, recommendations for educators and researchers, and a summary of the contribution of this study.

Summary of findings

The first research question was: ‘What engagement structures can be identified in high school students studying in a robotics course?’ In response to this question, we observed that our research sample (N = 41) exhibited 23 engagement structures in total, 12 of which were already known and existed in the literature, and 11 of which were novel (see Tables 2 , 3 , 4 for details). Previous studies that investigated engagement structures in other disciplines had also identified novel structures (Goldin, 2018 ; Perez & Verner, 2019 ; Verner & Revzin, 2017 ; Verner et al., 2013 ).

The second research question was ‘what are the commonalities and differences between engagement structures of the two groups of students—those preparing for academic engineering programs and those preparing for technical college?’ In response to this question, we observed that School A group (n 1  = 20) exhibited 17 engagement structures, and that School B group (n 2  = 21) exhibited 15 structures. The two groups shared nine known structures, but no novel structures: the six novel structures identified in School A group were not observed in School B group, and the five novel structures identified in School B group were not observed in School A group. We identified all the engagement structures that had appeared in the literature. See Tables 2 , 3 and 4 for details.

We identified three known engagement structures that differentiated between the two groups, namely ‘Don’t disrespect me’ and ‘I don’t want to learn it’ (School A group), and ‘Look how smart I am’ (School B group). Moreover, the two groups of students did not share any novel structures with each other; while School A group’s novel structures centred around their difficulties with learning the topic at hand, School B group’s novel structures centred around taking charge, or wanting to take charge of their own learning. These structures represent different perceptions of the learning experience and of oneself as a learner, similar to those we found for the known structures which the two groups did not share.

The distinction we found in engagement structures—both novel and previously reported—between the two groups warrants explanation. In our opinion, this distinction is rooted in the significant differences in prior academic achievement between the two groups of students. We did not have access to the data on the students’ achievements, and our inference about these differences is based on the evaluation of their prior knowledge given by the teachers. The situation, in which the academic performance of school students in technical tracks is lower than that in academic tracks, is quite typical (Małgorzata et al., 2018 ). Since, as known (Dong et al., 2020 ; Lee, 2014 ; Lei et al., 2018 ; Piñeiro et al., 2019 ) that student academic achievement and learning engagement are in direct relationship, we explain the observed differences in learning engagement in the two robotics courses by the differences in the level of students’ prior academic achievement in the two groups.

Related to this explanation, Wu and Huang ( 2007 ) reported that students who were low achievers tended to be disengaged when compared with high achieving students, when the instructional method was student-centred. Their findings corroborated our characterization of the novel engagement structures we found in both study groups. The high achievers expressed wanting to direct their own study (‘Look how smart I am’), whereas students in the lower achieving group expressed desire for further instructional support from the teacher. In the specific case of learning with robots, a potential compounding factor for low achieving students’ disengagement (‘Don’t disrespect me’, ‘I don’t want to learn it’) could be their difficulties in learning the new subject through the experiential learning cycle (Verner and Korchnoy 2006 ), due to their lack of reflective thinking and conceptual learning skills.

Learning with robots has benefits which are unrelated to students’ academic level, as well as benefits which are related to it: collaborative learning in robotic environments can benefit high achieving students by developing their teamwork skills, which are essential for the current workforce (Ananiadou & Claro, 2009 ; Korchnoy & Verner, 2010 ; National Research Council, 2012 ; Verner & Hershko, 2003 ), and for lower achieving students, the hands-on interdisciplinary activities with robots provide an effective way to learn STEM concepts and acquire technical skills (Cuperman & Verner, 2013 ; Spolaôr & Benitti, 2017 ).

Implications and recommendations

Our findings show that the study of student engagement should involve learning processes in different educational tracks, and involve groups of students with different achievement levels, as each group may exhibit distinct patterns of engagement. Another implication of our findings is that monitoring students’ engagement is of particular importance for experiential learning in robotic environments, as it allows the teacher to gain a fuller understanding of the students, as individuals and as a group.

This study can serve as a basis for further investigations into student engagement in robotics, and in particular for exploring students of various achievement levels. Teasing out differences in prior knowledge and in learning skills would make for an important contribution to the understanding of how engagement structures vary between students with different academic achievement levels. Future research could also build on the present study and explore student groups in other educational frameworks and contexts, such as middle-school or informal education. Observing and analysing the change in individual students’ engagement structures over time, as Verner and Revzin ( 2017 ), would be another potentially fruitful avenue of research.

The authors can make four recommendations for robotics teachers interested in improving their students’ learning engagement:

Identify each student’s internal motivations and use this understanding to facilitate students’ autonomy;

Monitor students’ classroom engagement: observe whether or not a student is paying attention, putting forth effort, enjoying class, exploring problems and solutions, and contributing constructively to classroom discussions;

Employ different strategies for introducing students to robotic technologies and concepts, thereby creating different entry points for engaging students with diverse interests and learning styles; and

For a given instructional scenario, ascertain the STEM knowledge and skills required for experiential learning with robots, and determine whether students have these knowledge and skills before deciding which instructional approach to apply.

Contribution

Whereas in most studies on student engagement, observations are conducted over a few months or less, this study involved an entire year of observations. This kind of systematic, long-term observation provides a more reliable portrayal of learning engagement, producing findings and conclusions which add to the theory of engagement structures. Another distinction of this study lies in being one of the first studies on high school robotics student engagement. The study contributes to understanding of student engagement in learning robotics for two different categories of high school students: low achieving students majoring in technology education, and high achieving students who study computer science, a natural science subject, and mechatronics.

The findings and conclusions of this study, and in particular the novel engagement structures and the distinctions that were identified between student achievement groups, can provide educators with a better understanding of their students’ needs and behaviours. This study can help teachers identify learning opportunities and more precisely tailor teaching robotics to their students. Additionally, the concept of engagement structures, along with those identified and explained herein, can be included in teacher education and in professional development programs.

With learning increasingly taking place in technology-rich environments, and being conducted remotely and online, the investigation of student engagement is becoming even more important than before. This is highly pertinent for robotics education, where physical hands-on activities with technological systems are the norm. Currently, a quick Google Scholar search for “robotics education” engagement reveals about 35,000 results, compared with, for example, “mathematics education” engagement , with more than 1,300,000 results. This indicates that there is still a large scope for investigation of student engagement in robotics education. We hope that this study will serve as a basis for future studies that will expand upon this topic.

Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6 (1), 63–71.

Google Scholar  

Alonso-Nuez, M. J., Gil-Lacruz, A. I., & Rosell-Martínez, J. (2020). Assessing evaluation: Why student engages or resists to active learning? International Journal of Technology and Design Education, 1–17.

Ananiadou, K., & Claro, M. (2009). 21st century skills and competences for new millennium learners in OECD countries.

Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A systematic review of studies on educational robotics. Journal of Pre-College Engineering Education Research, 9 (2), 2.

Article   Google Scholar  

Armier, D. D., Jr., Shepherd, C. E., & Skrabut, S. (2016). Using game elements to increase student engagement in course assignments. College Teaching, 64 (2), 64–72.

Barak, M. (2010). Motivating self-regulated learning in technology education. International Journal of Technology and Design Education, 20 (4), 381–401.

Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58 (3), 978–988.

Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26 (3–4), 369–398.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101.

Brown, L., Kerwin, R., & Howard, A. M. (2013). Applying behavioral strategies for student engagement using a robotic educational agent. In 2013 IEEE international conference on systems, man, and cybernetics (pp. 4360–4365).

Church, W. J., Ford, T., Perova, N., & Rogers, C. (2010). Physics with robotics—using LEGO MINDSTORMS in high school education. In 2010 AAAI Spring symposium series .

Code, J., Ralph, R., & Forde, K. (2020). Pandemic designs for the future: Perspectives of technology education teachers during COVID-19. Information and Learning Sciences .

Craft, A. M., & Capraro, R. M. (2017). Science, technology, engineering, and mathematics project-based learning: Merging rigor and relevance to increase student engagement. Electronic International Journal of Education, Arts, and Science, 3 (6), 142–158.

Creswell, J. W., Hanson, W. E., Clark Plano, V. L., & Morales, A. (2007). Qualitative research designs: Selection and implementation. The Counseling Psychologist, 35 (2), 236–264.

Cross, J. L., Hamner, E., Bartley, C., & Nourbakhsh, I. (2015). Arts & Bots: Application and outcomes of a secondary school robotics program. In 2015 IEEE frontiers in education conference (FIE) (pp. 1–9). IEEE.

Cuperman, D., & Verner, I. M. (2013). Learning through creating robotic models of biological systems. International Journal of Technology and Design Education, 23 (4), 849–866.

Dong, A., Jong, M. S. Y., & King, R. B. (2020). How does prior knowledge influence learning Engagement? The mediating roles of cognitive load and help-seeking. Frontiers in Psychology, 11 , 1–10.

Goldin, G. A. (2018). Discrete mathematics and the affective dimension of mathematical learning and engagement. In Teaching and learning discrete mathematics worldwide: Curriculum and research (pp. 53–65). Springer, Cham.

Goldin, G. A., Epstein, Y. M., Schorr, R. Y., & Warner, L. B. (2011). Beliefs and engagement structures: Behind the affective dimension of mathematical learning. ZDM, 43 (4), 547.

Green, J. L., Camilli, G., & Elmore, P. B. (Eds.). (2012). Handbook of complementary methods in education research . Routledge.

Han, S., & Bhattacharya, K. (2001). Constructionism. Learning by design, and project-based learning. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology . Retrieved June, 15, 2008.

Harel, I. E., & Papert, S. E. (1991). Constructionism . Ablex Publishing.

Hashimoto, T., Kobayashi, H., Polishuk, A., & Verner, I. (2013). Elementary science lesson delivered by robot. In 2013 8th ACM/IEEE international conference on human-robot interaction (HRI) (pp. 133–134). IEEE.

Hatch, J. A. (2002). Doing qualitative research in education settings. Suny Press. Jensen, E. (2013). How poverty affects classroom engagement. Educational Leadership, 70 (8), 24–30.

Jensen, E. (2013). How poverty affects classroom engagement. Educational Leadership, 70 (8), 24–30.

Jones, B. F., Rasmussen, C. M., & Moffitt, M. C. (1997). Real-life problem solving: A collaborative approach to interdisciplinary learning . American Psychological Association.

Kaburlasos, V. G., & Vrochidou, E. (2019). Social robots for pedagogical rehabilitation: Trends and novel modeling principles. In Cyber-physical systems for social applications (pp. 1–21). IGI global.

Kalina, C., & Powell, K. C. (2009). Cognitive and social constructivism: Developing tools for an effective classroom. Education, 130 (2), 241–250.

Kelly, S., & Price, H. (2009). Vocational education: A clean slate for disengaged students? Social Science Research, 38 (4), 810–825.

Khalil, D., Lake, E., & Johnson, A. (2019). Teachers’ classroom engagement structures: A comparative study of a novice US and an experienced UK mathematics teacher. In Affect and mathematics education (pp. 255–282). Springer, Cham.

Korchnoy, E., & Verner, I. M. (2010). Characteristics of learning computer-controlled mechanisms by teachers and students in a common laboratory environment. International Journal of Technology and Design Education, 20 (2), 217–237.

Lee, E., & Hannafin, M. J. (2016). A design framework for enhancing engagement in student-centered learning: Own it, learn it, and share it. Educational Technology Research and Development, 64 (4), 707–734.

Lee, J. S. (2014). The relationship between student engagement and academic performance: Is it a myth or reality? The Journal of Educational Research, 107 (3), 177–185.

Lei, H., Cui, Y., & Zhou, W. (2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: An International Journal, 46 (3), 517–528.

Liu, C. C., & Chen, I. (2010). Evolution of constructivism. Contemporary Issues in Education Research, 3 (4), 63–66.

Małgorzata, K., Tanja, B., & Simon, F. (2018). OECD reviews of vocational education and training apprenticeship and vocational education and training in Israel . OECD Publishing.

National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century . National Academies Press.

Newmann, F. M. (1992). Student engagement and achievement in American secondary schoo ls. Teachers College Press, 1234 Amsterdam Avenue, New York, NY 10027 (paperback: ISBN-0-8077-3182-X, $17.95; hardcover: ISBN-0-8077-3183-8, $38).

Papert, S. (1980). Mindstonns (p. 607). Basic Rooks.

Perez, H., & Verner, I. (2019). Engagement in learning through design and experimentation with robots. In International conference on interactive collaborative learning (pp. 297–305). Springer, Cham.

Piñeiro, I., Estévez, I., Freire, C., de Caso, A., Souto, A., & González-Sanmamed, M. (2019). The role of prior achievement as an antecedent to student homework engagement. Frontiers in Psychology, 10 (140), 1–8.

Reeve, J. (2012). A self-determination theory perspective on student engagement. In Handbook of research on student engagement (pp. 149–172). Springer, Boston, MA.

Reimers, F. M., & Schleicher, A. (2020). A framework to guide an education response to the COVID-19 Pandemic of 2020. OECD. Retrieved April, 14, 2020.

Reynolds, R., & Caperton, I. H. (2011). Contrasts in student engagement, meaning-making, dislikes, and challenges in a discovery-based program of game design learning. Educational Technology Research and Development, 59 (2), 267–289.

Rihtaršič, D., Avsec, S., & Kocijancic, S. (2016). Experiential learning of electronics subject matter in middle school robotics courses. International Journal of Technology and Design Education, 26 (2), 205–224.

Rusk, N., Resnick, M., Berg, R., & Pezalla-Granlund, M. (2008). New pathways into robotics: Strategies for broadening participation. Journal of Science Education and Technology, 17 (1), 59–69.

Schreiber, L. M., & Valle, B. E. (2013). Social constructivist teaching strategies in the small group classroom. Small Group Research, 44 (4), 395–411.

Scruggs, B. (2009). Constructivist practices to increase student engagement in the orchestra classroom. Music Educators Journal, 95 (4), 53–59.

Spolaôr, N., & Benitti, F. B. V. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education, 112 , 97–107.

Trowler, V. (2010). Student engagement literature review. The Higher Education Academy, 11 (1), 1–15.

Verner, I. (2013). Characteristics of student engagement in robotics. In FIRA RoboWorld congress (pp. 181–194). Springer, Berlin, Heidelberg.

Verner, I., & Korchnoy, E. (2006). Experiential learning through designing robots and motion behaviors: A tiered approach. International Journal of Engineering Education , 22 (4), 758–765.

Verner, I. M., & Hershko, E. (2003). School graduation project in robot design: A case study of team learning experiences and outcomes . 14, 2 (spring 2003).

Verner, I., Massarwe, K., & Bshouty, D. (2013). Constructs of engagement emerging in an ethnomathematically-based teacher education course. The Journal of Mathematical Behavior, 32 (3), 494–507.

Verner, I. M., & Revzin, L. B. (2017). Robotics in school chemistry laboratories. In Robotics in education (pp. 127–136). Springer, Cham.

Wu, H. K., & Huang, Y. L. (2007). Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education, 91 (5), 727–749.

You, Z. J., Shen, C. Y., Chang, C. W., Liu, B. J., & Chen, G. D. (2006). A robot as a teaching assistant in an English class. In Sixth IEEE international conference on advanced learning technologies (ICALT'06) (pp. 87–91).

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Verner, I.M., Perez, H. & Lavi, R. Characteristics of student engagement in high-school robotics courses. Int J Technol Des Educ 32 , 2129–2150 (2022). https://doi.org/10.1007/s10798-021-09688-0

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11 Best Robotics Programs for High School Students

By Alex Yang

Graduate student at Southern Methodist University

8 minute read

Robotics programs for high school students offer a dynamic learning experience that combines STEM with hands-on creativity. By participating in robotics programs, high schoolers gain practical skills in programming, electronics, and mechanical design. Additionally, robotics programs can encourage innovation and inspire a passion for technology. These programs also tend to have an emphasis on hands-on projects such as building or programming your own robot, which can be a refreshing difference from programs that are more lecture heavy.

Whether you’ve already been tinkering with robots or are interested in getting started, there’s a great robotics program out there for you!

List of Robotics Programs for Teens

Summer program in automation, robotics, and coding (sparc).

Hosting institution: NYU Tandon

Location: New York City

Deadline: April 20 (closed)

Cost: $2,825

SPARC introduces students to the basics of robotics, mechatronics and programming. This is a 2 week, full-day, in-person summer program for rising 9th through 12th grade high school students. You do not need experience in robotics to participate in SPARC; during the program, students will learn about applications for microcontrollers, interface sensors and actuators, basic electrical components, circuits, circuit configurations, and much more.

This is a great program to start learning robotics at a high level. Unfortunately, financial aid and scholarships are unavailable for the program.

Education Unlimited Robotics

Hosting institution: Stanford University

Location: Palo Alto, California

Deadline: N/A (but sessions still open to enroll for summer 2023)

Cost: $3,025 for Extended Day Camp, $3,560 for Overnight Camp

In this hands-on, one-week program, students will use the VEX5 robotics platform to construct a fully functional robot to overcome obstacles. Throughout this robotics program, students will explore electrical circuits, power systems, and communication setups. 

If competition is also your thing, the program concludes with a finale presentation focused on designing a robotics sports competition! This program is a great opportunity to build a robot and put it through a series of challenges all in one week. Students have the option to participate in either the overnight program (staying in dorms) or the extended day (from 9 am - 9 pm).

Engineering and Robotics at MIT

Hosting institution: MIT and National Geographic

Location: Cambridge, Massachusetts

Cost: $6,495 + airfare

In this 10 day program hosted in partnership with National Geographic Explorers, students participate in seminars covering diverse subjects, such as remote exploration of space and sea and engineering microscopic robots. Students then take those experiences to develop a capstone project proposal that uses advanced science to tackle a significant problem in their local community.

Some of this high school robotics program’s highlights include being able to meet with actual researchers in robotics and also using 3D printers to bring your own designs to life. Although this program is on the more expensive side, it does offer you the opportunity to visit and use state of the art research labs that you otherwise wouldn’t have access to.

American Robotics Academy Summer Camp

Hosting institution: American Robotics Academy

Location: Texas and North Carolina, Live Virtual classes also offered

Cost: Starting at $195; varies based on location and camp type

If LEGO is something you’re passionate about, students in this program are provided a LEGO Technic Kit, and put their robots through different competitions. The Technic Kit includes various specialized elements like axles, gears, beams, wheels, motors, microcomputers, pneumatics, and more. While this camp is geared more towards younger kids in elementary and middle school grades, this can be an exciting opportunity if you’re interested in LEGO.

Hosting institution: Worcester Polytechnic Institute

Location: Worcester, Massachusetts

Deadline: Closed for Summer 2023

Cost: $795 (including lunch)

This one week commuter program for students entering Grades 9-11 offers the opportunity to delve into technology and work on hands-on projects with WPI faculty. In their robotics engineering courses, students can explore concepts of mechanical and electrical engineering, as well as computer science. Through several projects, you’ll learn about the use of motors, mechanisms, sensors, and programming in robotics (specifically the C programming language). 

This program is a bit limited to students living in the New England area since it’s only a commuter program, but if you live in the area this could be an interesting program!

Machine Learning (ML)

Cost: $2,600 + optional housing or meal plan packages

If you’re interested in NYC, this is a great robotics program hosted by Tandon. This two-week summer program introduces students to machine learning, computer science, and artificial intelligence. Lessons cover video and image recognition technologies; interactive voice controls for homes; autonomous vehicles; real-time monitoring and traffic control; cutting-edge diagnostic medical technologies, and other technologies that are a part of our daily lives.

Students learn how logic and mathematics are applied to ML, and there’s also a strong emphasis on how these techniques can be applied to solving real world challenges. This makes the ML program stand out as an impressive summer robotics program for high schoolers.

Northeastern Young Scholars Program

Hosting institution: Northeastern University

Location: Boston, Massachusetts

Deadline: Closed for Summer 2023; Opens January 2024

Cost: Free and for permanent Massachusetts residents only

This program is limited and only open to permanent Massachusetts residents who are entering 12th grade. However, if you are a Massachusetts resident, this program offers hands-on research experience with specific Northeastern professors.

The program runs from late June to early August, and past students have conducted research in compression algorithms, battery chemistries, and evaluating new cancer therapies. What also sets this program apart is its focus on career exploration for students, with special seminars that introduce students to different careers. Further, each Young Scholar participates in college and career counseling during the program.

UT Austin Academy for Robotics

Hosting institution: UT Austin

Location: Austin, Texas

Deadline: Closed for Summer 2023 

Cost: $2,100

This one week credit-bearing program will introduce students to C++ and Arduino programming, and all skill levels are welcome to the program. There will also be various hands-on projects that students can participate in, including: competing in a robot race, conducting robotics projects in simulation and assembling a Bot’ n Roll robot.

The program also has a heavy emphasis on experiencing the UT Austin campus. Although we agree that it definitely is a great opportunity if you’re interested in the college, note that programs like these will never boost your chances of admission into the school. Be sure to keep that in mind as you think about what summer programs you want to attend.

Embry Riddle Robotics & Autonomous Systems Camp

Hosting institution: Embry Riddle Aeronautical University

Location: Daytona Beach, Florida

Deadline: Opens in January for 2024

In this one week program, students have the opportunity to design, program, and test their own autonomous robots with the help of Embry Riddle faculty. For high school students who are based in the Southeastern US, this could be a unique robotics program option to explore for the summer.

Saint Louis Robotics Summer Academy

Hosting institution: Saint Louis University

Location: Saint Louis, Missouri

Deadline: Applications for Summer 2024 open in Feb 2024

For this one week, day camp-only program, students work in teams to build their own custom robots. This camp also offers the opportunity to meet with current Saint Louis University engineering students and faculty, and explore the university’s various labs and facilities. As a small bonus, students also get to take home the robot that they build!

This program has explicitly mentioned that they only have space for 25 students, which allows for a potentially close knit environment amongst students, but also might require students to apply as early as possible for the best chance of admission.

iD Tech Coding and AI Academy Camp

Hosting institution: MIT or Stanford

Location: Various

Cost: Starting at $4,599

This camp dives into the Sphero RVR robot, a programmable robot that students work with throughout the program to develop an autonomous vehicle. Students create computer algorithms to program the self-driving vehicle. One strength of this iD Tech program is the opportunity to learn from instructors with strong industry backgrounds at companies like Microsoft, Google, and Disney.

Choosing Your Robotics Study Program 

There are many different robotic programs for high school students, and choosing the one that’s best for you may depend on geographical location, the cost of the program, and what you want to get out of the experience. As you explore these robotics programs, consider what your goals and preferences are. Do you want to experience a college campus environment while doing the program? Do you want to participate in robot competitions? No matter which you choose, you’re sure to have an amazing time!

If you want to pursue independent research in robotics topics, Polygence is a great option. Our research program mentors have worked with student alumni on robotics-related projects, including: researching flying robot dynamics ; assessing the role of robots in space exploration ; and constructing robot arm simulations .

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Your passion can be your college admissions edge! Polygence provides high schoolers a personalized, flexible research experience proven to boost your admission odds. Get matched to a mentor now!"

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Characteristics of student engagement in high-school robotics courses

Igor m. verner.

1 Faculty of Education in Technology and Science, Technion – Israel Institute of Technology, Haifa, Israel

Huberth Perez

2 School of Engineering, Massachusetts Institute of Technology, Boston, MA USA

Student engagement has been described as active involvement in a learning activity that significantly affects learning achievement. This study investigated student engagement in robotics education, considering it as an instant emotional reaction on interaction with the teacher, the peers, and the robotic environment. The objective was to characterize engagement in high school robotics courses through the lenses of preparation for academic and technical careers. Students who participated in this study (N = 41), all of whom were in the eleventh grade, belonged to either School A (n 1  = 20) or School B (n 2  = 21). School A students studied only one subject at an advanced level—mechatronics, while each student in School B studied engineering systems as well as one of the following three subjects at an advanced level: computer science, a natural science subject, or mathematics. Data were collected via structured classroom observations, interviews, and a questionnaire. From the analysis of the collected data, we identified 23 engagement structures in total, 12 of which were already known in the literature, and 11 of which were novel. The two groups of students shared nine known structures, and no novel structures. Unlike previous studies of engagement structures, this study was based on an entire year of observations. Additionally, it is one of the first studies of high school student engagement in robotics education. Our findings and conclusions contribute to understanding of student engagement in robotic education, allowing robotics teachers to tailor their instruction more effectively.

Introduction

In recent years, demand for robots has been rising, driven by new developments in automation and computing. This rise in demand has been accompanied by a growing prevalence of robotics education in schools (Anwar et al., 2019 ). Robotics is considered as an avenue through which students can actively engage in learning concepts from different disciplines (Church et al., 2010 ; Cross et al., 2015 ; Korchnoy & Verner, 2010 ; Verner & Hershko, 2003 ). More than that, the students can develop the skills of adaptability, self-regulation, learning in digital technology environments, and other skills (Ananiadou & Claro, 2009 ; Benitti, 2012 ; National Research Council, 2012 ) that are vitally needed for learning in the era of the COVID-19 pandemic.

The term student engagement has been used to describe the level of active involvement of students in their learning tasks and activities. Engagement largely determines student–teacher interactions, class atmosphere, and learning outcomes. Research indicates that engagement is positively linked with student’s prior academic achievement (Dong et al., 2020 ; Lee, 2014 ; Lei et al., 2018 ; Piñeiro et al., 2019 ). Fortunately, there are many pathways into robotics that can engage students of different interests and backgrounds (Alimisis, 2013 ; Rusk et al., 2008 ). Researchers have observed students performing robotics tasks dedicating extra time to solving robotics problems, as students were trying to find solutions by themselves (You et al., 2006 ).

Need and rationale for study

Technology is an optional subject in Israel. About one-third of Israeli high school students choose to study technology subjects. Out of those students, about one-third of those students choose the track that prepares them for academic engineering programs, another third—the track preparing them for technical college, and the remaining third—the track preparing them for working life. The first track is usually open only to students who excel academically, while the latter two tracks do not require high levels of academic achievement (Małgorzata et al., 2018 ).

Robotics educators adapt the subject for school students of different academic achievement levels. In educating high achieving students, educators emphasise career readiness and twenty-first century skills, while for lower achieving students, robotics serves a tool for experiential learning of STEM concepts (Spolaôr & Benitti, 2017 ; Verner & Revzin, 2017 ) and for strengthening self-efficacy beliefs (Barak, 2010 ).

The potential of robotic learning environments for motivating students has been recognized in the literature (e.g., Brown et al., 2013 ; Hashimoto et al., 2013 ). However, student engagement in distinct relation to the learning pathways described above has not been investigated. Unlike the present study, our previous studies of student engagement in robotics education did not include multiple long-term observations of student behaviour (Verner, 2013 ; Verner & Revzin, 2017 ). These studies began with the analysis of high achieving middle school students’ engagement in an outreach robotics course (Verner, 2013 ). Then, in the study of learning engagement of low achieving high school students majoring in technology education, we came up with an idea to apply the theory of engagement structures.

The construct of engagement structures was introduced by Goldin et al. ( 2011 ) to describe common types of engagement which are triggered by the instant motivational desires of students engaged in learning mathematics. Since its introduction, the construct of engagement structures has been investigated in several studies of learning engagement (e.g., Craft & Capraro, 2017 ; Khalil et al., 2019 ; Perez & Verner, 2019 ; Verner & Revzin, 2017 ; Verner et al., 2013 ).

Research objective and research questions

The objective of this study was to characterize engagement in high school robotics courses through the lenses of preparation for academic and technical careers. The research questions were:

• What engagement structures can be identified in high school students studying in a robotics course?
• What are the commonalities and differences between engagement structures of the two groups of students—those preparing for academic engineering programs and those preparing for technical college?

The rest of this manuscript is structured as follows: first, an overview of the literature on student engagement, learning theories and instructional methods in robotics education is provided. Next, the methods of this study are detailed and explained, followed by its findings. The last is the discussion, where the key findings are elaborated on, the limitations and contribution of the study are described, the implications of this study for robotics education are outlined, and recommendations for educators and for researchers are provided.

Student engagement and robotics education

This section discusses selected literature on student engagement, engagement structures, the impact of engagement on academic achievement, and student engagement in robotic environments.

Student engagement

The subject of student engagement has been widely discussed in literature and conceptualized from many different perspectives (Trowler, 2010 ). In this paper we will mention only those aspects of the subject that are most relevant for our study. Newman defined student engagement as “the student’s psychological investment in and effort directed toward learning, understanding, or mastering the knowledge, skills, or crafts that academic work is intended to promote” (Newmann, 1992 , p. 12). Behavioural, cognitive, and affective engagement of students in their learning activities largely determines learning outcomes, student–teacher interactions, and class atmosphere (Lei et al., 2018 ). Student engagement cannot be separated or disentangled from the social context in which it occurs; it is a joint product of his or her motivation and classroom support or thwarting (Reeve, 2012 ).

Engagement structures

Goldin et al. ( 2011 ) focused on the study of student engagement that can be referred to as an instant emotional reaction on interaction with the teacher, the peers, or the learning environment. The construct of an engagement structure, introduced by them, is intended to describe specific types of student engagement that are typically observed in different categories of students and certain learning situations. The authors proposed to identify and determine engagement structures by inductive analysis of student behaviours repeatedly observed during the learning activities.

The construct of an engagement structure includes the following components: the statement of the motivating desire, the scheme of the social interaction, the characteristics of situations which are likely to evoke the desire, and the behavioural patterns being exhibited. The statement of the motivating desire is formulated so, as to represent a certain typical kind of motivating desires that can be observed in different categories of students and in different subjects. The other components of the engagement structure represent motivating desires that are typically observed in a specific category of students learning a specific subject.

Goldin et al. used engagement structures to characterise student engagement in urban middle school mathematics classrooms. Furthermore, the authors proposed a general approach on how to investigate engagement and called to identify and determine engagement structures in different learning activities. Verner et al. ( 2013 ) applied the construct of engagement structures to the study of engagement in an ethno-mathematically based teacher education course. They observed some of the engagement structures described by Goldin et al. ( 2011 ) and identified a new structure related to cultural identity of students. Verner and Revzin ( 2017 ) applied the theory for the study of engagement in an automated chemistry laboratory course. The study indicated the presence of some of the engagement structures found by Goldin et al. and discovered three new structures specific to the category of learners and type of learning environment.

The engagement structures found in the studies mentioned above are summarized in Table ​ Table1 1 .

a Verner et al. ( 2013 )

b Goldin et al. ( 2011 ), Verner et al. ( 2013 ), Verner and Revzin ( 2017 )

c Goldin et al. ( 2011 ) , Verner and Revzin ( 2017 )

d Verner and Revzin ( 2017 )

e Goldin et al. ( 2011 )

Engagement and achievement

Engagement has been positively linked to student academic achievement (Lee, 2014 ). For example, Wu and Huang ( 2007 ) investigated 9th-grade cognitive, emotional, and behavioural engagement in teacher-centred and student-centred technology-enhanced classrooms. The authors reported that low-achieving students in the student-centred group learning demonstrated disengaged behaviours and participated in few conceptual discussions. This low-level engagement seemed to have a negative impact on their learning achievements. The findings suggest that there is no universal instructional approach suitable for engaging every student.

Previous studies have found that lower achieving students tend to exhibit less engagement with learning than higher achieving students (Jensen 2013 ) and that student perception of the school environment affects their academic motivation and engagement (Alonso-Nuez et al., 2020 ). Students on vocational tracks tend to be lower achievers than students who excel academically (Kelly & Price, 2009 ).

Self-determination theory assumes that all students possess inherent growth needs that provide a motivational foundation for their high-quality classroom engagement and positive school functioning. This theory emphasizes the instructional task of vitalizing students’ inner motivational resources as the key step in facilitating high-quality engagement. It identifies the inner motivational resources that all students possess, and offers recommendations as to how teachers can mobilize, nurture, and vitalize these resources during the instruction to facilitate high-quality student engagement (Reeve, 2012 ).

Learning engagement in robotic environments

One of the most important duties of a teacher is to offer students opportunities for hands-on exploration and to provide tools to construct knowledge (Alimisis, 2013 ). This knowledge construction can happen most effectively in a context where the student is engaged in constructing a technological artefact (Papert, 1980 ). The wide range of robotics applications can engage students with different interests and from various backgrounds (Alimisis, 2013 ; Rusk et al., 2008 ). One of the reasons behind the apparent improvements in student engagement when learning with robots is that robots are especially appealing to students due to their physical form and novelty (Kaburlasos & Vrochidou, 2019 ).

Learning with robots can benefit students of different academic levels. Collaborative learning in robotic environments can benefit high achieving students by developing their teamwork skills, which are essential for the current workforce (Ananiadou & Claro, 2009 ; Korchnoy & Verner, 2010 ; National Research Council, 2012 ; Verner & Hershko, 2003 ). For lower achieving students the hands-on interdisciplinary activities with robots provide an effective way to learn STEM concepts and acquire technical skills (Cuperman & Verner, 2013 ; Spolaôr & Benitti, 2017 ).

Brown et al. ( 2013 ) applied behavioural strategies for engagement using robotic educational agents. In the study, the authors focused on a testing scenario to evaluate the role and efficacy of engagement using a robotic agent. In order to enable human interaction, the robot was programmed with a range of verbal and nonverbal behaviours. The authors reported that the approach effectively eliminates idle time and keeps the student engaged in the allocated tasks.

Verner and Revzin ( 2017 ) proposed an educational approach in which high school students majoring in mechanical engineering will be involved in the development of robotic laboratory devices and use them for chemical experiments. The authors observed learning activities at different stages of the course and characterized students’ engagement structures throughout these stages. They concluded that engagement structures can evolve and do not have to remain fixed within students.

In this section we explain the research methodology and describe setting of the study and participant sample. Next, we give a detailed description of the robotics courses followed up in this study, the instruments of data collection, and the analyses of collected data. Finally, we outline how ethical standards were upheld for the study.

Research methodology

Technology education is a broad field which includes various disciplines and engages different categories of students. This high level of variance presents challenges for studying learner engagement from a general perspective, even in just one discipline such as robotics education. Taking this breadth and diversity into consideration, the research methodology chosen for this study can be best described as multiple case studies, in which data about a highly context-dependent phenomenon is collected from multiple cases (Creswell et al., 2007 ). This study investigates two groups of students in the high school mechanical engineering track: one group from a program meant to prepare students for technical careers, and another group in a program meant to prepare students for academic studies in engineering. Thusly, the present study covers two ends of the spectrum in high school robotics education, providing a broader perspective of the phenomenon under investigation than in the case of a single case study.

In Israel robotics is taught in schools in different ways, making valid quantitative comparisons between different schools difficult. Even in one robotics class, the diversity of experiential activities and the difficulty to quantitatively analyse students’ engagement led us to opt for using a qualitative approach. More specifically, the researchers employed engagement structures as a theoretical framework and applied a theory-driven, top-down approach (Braun and Clarke, 2006 ) for analysing the collected data. Previous empirical studies on engagement structures have also opted for a qualitative approach (e.g., Verner et al., 2013 ).

Research setting

The study took place at two locations in Haifa, a city in the north of Israel. The first location was in the Technology Laboratory of the University Faculty of Education in Science and Technology. The second location of the study was at an urban comprehensive private school. In each location, a different group of high school students was taught a robotics course by their respective teacher. The curricula were different, but both ground on design and inquiry activities with robots.

School A is an urban public school. Due to a recent change of location and some funding challenges, it had no laboratory facilities. The University afforded the school with the opportunity to conduct the robotics course at the Technology Laboratory of the University.

School B is an urban private comprehensive school. Recently, the school opened an elective program for high school technology in robotics and engineering systems. Lessons in this program are conducted in the school’s own technology laboratory and two classrooms.

Research sample

Students who participated in this study (N = 41) belonged to one of two schools: School A or School B. School A students (n 1  = 20) were all 11th graders—12 girls and eight boys, studying robotics as part of high school mechatronics track, with the specific aim of providing students with the necessary background in science and technology required for technician studies following their high school graduation. most of the students in the School A group were not accepted into more prestigious high school tracks, such as computer science or natural sciences. The teacher of this class was a veteran physics teacher and Ph.D. holder with research and practical experience in teaching students who are not high achievers in science and technology. He was tasked by the school to design and teach a robotics course specifically for the students in question.

School B students (n 2  = 21) were all 11th graders—19 boys and two girls. All of them were matriculating in computer science, a natural science subject, or mathematics. Recently, the school established an elective robotics and engineering systems program for high school students, with the specific aim of preparing students for academic studies in engineering. School B students who participated in this study were taught by an engineering teacher with extensive professional experience in the hi-tech industry.

The courses

While both courses—in School A and in School B—centred on experiential learning, design and inquiry in robotic environments, the overall aim of each course was different: in the case of the course taught to School A’s students, the overall aim was to teach them basic scientific concepts through experimentation with robots, while in the case of the course taught to School B, the overall aim was to teach students how to solve real-life problems by designing and building robots. The students in School B had already learned the basics of robotics in the 10th grade, and therefore did not need to learn this as the students in School A did.

The objectives of the course were to (1) know the principles of motor operation, mechanical transmissions, sensors, and open- and closed-loop control, (2) understand the scientific and mathematical concepts involved in the design and operation of robotic models, (3) acquire basic skills in design and analysis of robotic models, and (4) acquire basic skills of scientific experimentation. Learning activities revolved around several assignments that the students performed individually or in pairs. In addition, the students executed three robotics projects. These projects tied back to the overall aim of the course, which was to teach students basic scientific concepts through robots.

The course progression was as follows: (1) presenting a physical concept to students, (2) guiding students in planning of experiment, (3) teaching students the relevant concepts and analysis tools, (4) guiding students in conducting experiment and collecting data, (5) guiding students in analyzing collected data, (6) guiding students in preparing the assignment report, and (7) guiding students in presentation of the assignment.

When performing the assignments, the students used different technological tools. They constructed robots from the Lego Mindstorms EV3 kits and 3D printed the needed additional parts using the MakerBot Replicator (Fig.  1 A). The students programmed robots with MindStorms software. In physical experiments, they collected and processed data from robot sensors by means of Fourier’s Data Logger (Fig.  1 B).

Students from School A at work: A Robot construction; B Making an experiment

The objectives of the School B group course were to (1) understand the principles of robot motion and control, (2) acquire basic skills of robot construction, programming, and operation, (3) acquire basic skills of 3D design and printing, (4) develop applied problem-solving skills.

The course progression was as follows: (1) learning fundamental concepts in robotics and applying them, (2) designing and implementing a given electronics-based project, (3) designing and implementing a robotics solution to a challenge posed by a commercial company, and (4) redesigning part of the classroom to introduce a robotics component, e.g., a smart screen. Throughout the semester, students were introduced to technological innovations and were taught ad-hoc topics that the teacher deemed as required for strengthening their proficiency as designers and implementors of robotic projects.

The students used a wide set of technological tools. They constructed robots from the Lego Mindstorms EV3 kits. The Arduino Uno and Raspberry Pi controllers were used for programming robot behaviours, remote control, and statistical analysis. The students designed the needed additional parts with SolidWorks and produced them in plastic using TierTime 3D printer or in wood and metal using CNC, grinding, and drilling machines. Figure  2 shows images of students while carrying out project activities.

School B students at work on robot projects: A Design; B Construction; C Guidance

Data collection and analysis

Data collection was carried out over the entire school year, resulting in frequent, long-term observations of student behaviours. Data were collected by the second author, who acted as an observer in the classrooms. Importantly, he was not involved in the design or teaching of any of the course curricula.

For each of the two student groups, data were collected thusly: throughout each lesson, indications of engagement in robotics activities were identified and recorded in the observation protocol. This protocol was developed by two of the co-authors based on Hatch ( 2002 ). The observation protocol form included the following data: date, participating students, lesson topic, lesson objective, project assignment, lesson assignments, learned concepts, problems solved by the students, teacher's interaction with the students, notable episodes of student engagement. At the end of each lesson, the in-class observer discussed the observations made during that lesson with the teacher, thus supplementing them with the teacher’s interpretation of the collected data. At the end of the course, a questionnaire was deployed among individual students, followed by individual student interviews, both intended for verifying and supplementing previously collected data.

Data analysis was carried out in the following way. We selected from among the noticed episodes of student engagement those pertaining to each engagement structure known from the literature and used the episodes to characterize the structure in the considered context. Then, we analysed the episodes that did not match the known engagement structures. We selected episodes that reflected a certain common pattern of learning engagement and formulated an engagement structure that expressed the pattern. In formulating engagement structures, we followed the recommendations of Goldin et al. ( 2011 ).

The researchers involved the respective teacher of each participant group in the analysis. The teacher’s point of view is naturally important, since they are familiar with students’ behaviour and can also provide context for it based on their familiarity with their students.

Based on the observations recorded by the in-class observer, the authors held regular meetings in which they discussed episodes of the learning process that provided evidence of engagement or disengagement were discerned, and subsequently analysed those episodes to determine patterns of learning engagement. For each pattern, they first examined whether it fits one of the engagement structures already described in the literature (see Table ​ Table1); 1 ); in cases where they could not identify any such structure, they tried to ascertain whether a novel engagement structure can be described. The discussion with the teacher at the end of each lesson concerned the patterns of engagement observed by both the teacher and researcher, with the teacher often providing the interviewer with explanations pertaining to student intentions and possible reasons behind their behaviour.

The engagement structures identified using the observation protocol and teacher interviews were verified and supplemented using a questionnaire (see Appendix), which was deployed to individual students following the end of the course, after the final course grade was provided to all the students. The questionnaire included open-ended questions intended to gather data on engagement structures already known and found in the existing literature, whether these were already identified using the previous tools or not. Each item in the questionnaire pertained to one distinct engagement structure.

Finally, the engagement structures that were identified using the previously collected data, whether already known or newly discovered, were verified and supplemented with further data concerning internal factors of engagement—such as desires and beliefs—by conducting an open-ended interview with each student. Green et al., ( 2012 ) noted that the advantage of an open-ended interview is that it allows extending and clarifying the informant’s responses through probing.

Study ethics

Ethical clearance and permissions for the study were obtained from the University Behavioural Sciences Research Ethics Committee and from the Ministry of Education Office of the Chief Scientist. The goal and method of our study was explained to the participating students in advance, and they each were told explicitly that they had the right to cease their participation and exit the study with no explanation or ramifications. Participants' identities were kept anonymous. In-class observations were made with the consent of the schools’ teachers and with the permission of the schools’ respective principals. Participating students and their legal guardians signed A letter of consent to participate in the research. Due to Ministry of Education guidelines for research studies, we were not able to obtain students’ grades.

This section gives an account of the engagement structures which the researchers had identified in students, based on the collected and analysed data. The section is divided into three sub-sections: first, we discuss and illustrate by examples the engagement structures that are described in literature and identified in our study; second, we present novel engagement structures not mentioned in literature (to the best of the authors’ knowledge); and third, we compare the engagement structures identified in the two groups to indicate the differences in their learning engagement.

Throughout this section, participant codes are used instead of names; each code starts with a letter denoting the group, and a number denoting the student in the list of participants. For example: A03 is a student #3 in the list of students from School A.

Existing engagement structures

The researchers identified 12 engagement structures already described in the literature (see Table ​ Table1) 1 ) and indicated in our study. Three out of these structures were observed in one of the groups, with the rest identified in both groups. Due to restrictions by the Israel Ministry of Education, student names could not be taken in the questionnaire, and thus could not be identified.

Table ​ Table2 2 presents these engagement structures with representative quotes from the interviews given by students from the groups where the structures were identifiedNovel engagement structures

Known engagement structures identified in both groups

a Engagement structures identified in students of both groups

b Engagement structures identified in students of Group A

c Engagement structures identified in students of Group B

The researchers identified 11 engagement structures which they did not find in the literature. Interestingly, not one of these novel structures was common to both groups of students: six novel structures listed in Table ​ Table3 3 were identified in the School A group.

Novel engagement structures identified in School A group

*The authors do not have the student ID on file

The five novel structures identified in the School B group are presented in Table ​ Table4 4 .

Novel engagement structures identified in School B group

The following are detailed descriptions of the novel engagement structures the researchers had identified in the School A group.

Explain in my native language

As repeatedly observed during the course, the students of the School A group experienced difficulties in understanding the concepts taught by the teacher in Hebrew. They expressed confusion, especially when the teacher used higher vocabulary. When the teacher, wishing to help the students, switched to Russian, they rejoiced and more deeply engaged in learning.

I learn with my body

The researchers observed episodes when the students were especially engaged in learning when it involved bodily experiences to examine physics concepts and ideas. This typically happened at the stage when the students learned concepts and analysed tools to be applied in the robotic assignments. For instance, A17—a female student—and two other students [A16, A18] had difficulties in understanding some physics concepts, so the teacher let the students examine these concepts through bodily experiences. In one of the lessons, A17 and the other two students found it hard to learn the concept of the centre of gravity (COG). The teacher asked the students to sit in a chair with their back against the chair and their feet on the floor. Then he asked the students to try to stand up with their back straight. After the students tried unsuccessfully, he explained why this did not work in terms of COG. The students smiled and expressed that after those experiences, they understood the concept. From there, they were engaged in the assignment for the rest of the lesson.

Based on the interview comments, for these students, it was very important to include this kind of activity in their learning process. The teacher noted that he found the effectiveness of such kind of group activities for these students.

Let me learn on my own

The observations showed that some students were engaged in the learning activities only when they worked alone; for example, A12, a male student who showed low levels of engagement in the beginning of the course. A12’s classmate [A15] distracted him from learning by talking about topics unrelated to the learning activity. In one of the lessons, [A15] was absent, and A12 worked alone. He was observed in this lesson to be closely engaged in the assignment and performed it well. Since then, with the consent of A12, the teacher has let him work alone and perform assignments individually. The teacher commented that he repeatedly observed this learning behaviour. His solution was to reorganize the teams and let such students work alone. In the interview, A12 said that before the interview, he did not pay attention to this fact, but when the researcher asked him about it, he realized that he worked better when he worked alone.

Please teach me

The researchers observed episodes when the students were not confident in performing the assignment and only worked under the teacher’s guidance. For instance, A14 was a female student who wanted neither to work alone nor with a teammate, but only when the teacher tutored her personally. Sometimes, the teacher did not help her much, but his presence made the student feel confident in performing the task. This observation was supported by the comments that the student gave in the interview.

The teacher commented that from his experience, this behaviour was quite typical in the first stage of the course. In his opinion, it is essential to provide such students with individual guidance. Therefore, he recommended involving a second teacher or at least a teacher’s assistant in the class. Even if the group only numbers 12 students, the attention they demand is beyond what can be provided by one teacher.

This is hard for me

Some students claimed that they did not have the background knowledge needed to perform the assignment. For example, in one instance when the teacher explained the task to A11, a female student, she just drew a Cartesian plane in her notebook and claimed that she was unable to perform this task. She said that she lacked the knowledge and skills in mathematics and physics needed for the task. We repeatedly observed this type of behaviour in this student during the course. The teacher's strategy was to strengthen the student's confidence. In the beginning, he gave her simple tasks with full support. Then, each lesson, the teacher gave her less support, increased the complexity level of the tasks, and assigned more responsibility to the student. At the end of the year, the student was able to carry out the project with little support and presented it to the class. It was a significant achievement considering that at the beginning of the course, she claimed she was not even able to construct a simple robotic model.

The teacher noted that nurturing of student's self-confidence is a long-term process. In the above example this process took eleven months for the student to achieve some level of self-confidence and self-dependence.

This is boring

We observed students who refused to train the skill by exercising it. For example, A20 was a male student who complained that the core of the assignments always was the same. The point is that when the teacher asked the student to do a new task using the skills that were acquired through the “boring” activities, he showed a lack of the skills and simply did not know what to do. The teacher’s strategy was to let the student see the value of knowledge and skills learned in the course and the importance of training through exercise and through practical activities. This observation was supported by the comments that A20 gave in the interview, where he reinforced the fact that he did the same and it was boring for him.

The teacher commented that it is typical for some students in this category to claim that they “already know everything,” but in fact, when the teacher asked them to perform a task, sometimes these students did not know what to do. Therefore, in the teacher's opinion, it is crucial to engage such students in experiential learning, which includes training by exercise.

Below are detailed descriptions of the novel engagement structures identified in the School B group.

I’m doing this only while it’s fun

From the observations, there were episodes in which students were engaged in learning assignments when they considered them as play activities. For instance, B07 was a male student who perceived the project as a game to be played. He became interested when the task was to build something for the robotic model. At some point, he stopped working and became disengaged with the learning activity. He explained to the researcher that he enjoyed the activity but did not want to spend the entire lesson on it, as it would not be fun. He did not show interest in programming and theoretical lessons, but he liked the electronics activities. He said that in other subjects like physics, he just solved problems and learned theoretical topics; on the other hand, when learning engineering systems, he built interesting things and applied everything he saw in the physics class. So, he enjoyed the lessons, and thought they were fun.

The teacher said that he could not understand this student. The student always said he liked the course, and that he felt excited about the projects. However, the teacher had to attract his attention continually and the student always asked what to do next, even if the teacher already explained the assignment. The teacher commented that on one hand, the student claimed that he was excited, but on the other hand, he always needed someone to push him back to performing the assignment.

I am just an assistant

We observed students who were deeply engaged in the projects but did not performed, by themselves, hands on activities of robot construction and programming. These students only assisted to their teammates. If the teacher required them to program a sensor or develop part of the mechanism in the robotic projects, they didn’t like it and became disengaged. For instance, B04—a male student—avoided getting involved in hands on activities. He liked to support his classmates by painting, writing project-related texts, designing the poster and the slides for the presentation, etc. If peers asked B04 to help in construction or programming, then he agreed and engaged in the activity. It was difficult to engage him in learning in any other way.

The teacher commented that this episode shows his difficulties in teaching the student who did not want to learn robotics but did want to assist his classmates. The teacher also commented that he knew B04's parents and they had pressured B04 to join the course as it gives bonus points to enter the University. Another reason to join the course was that B04’s friends had also taken it and B04 wanted to be with them. The teacher was aware that the student used to do things less related to the project and acted as an assistant without getting involved in the task. He confessed that sometimes allowed B04 to perform activities such as designing a poster, which was not exactly part of the course's curriculum. However, it was the only way the teacher could keep him engaged in the course's activities. In the teacher’s opinion, the student was not happy with the course.

I don’t like being corrected

We identified episodes in which students were engaged in the assignment, but when the teacher pointed out mistakes in their work, the students reacted negatively to the comments, felt offended, and in consequence turned to disengage for a period from one hour up to the rest of the lesson. For example, B06 was a male student who was deeply engaged in the projects. However, when the teacher looked at his work and found errors, the student became frustrated and upset. B06 stopped working on the project and turned to web surfing and talking with peers on topics unconnected to the assignment. He was disengaged for quite a long time and only returned to work on the project at the end of the lesson. Such episodes were repeatedly observed during the course.

The teacher noted that students of this category are accustomed to achieving high grades and receiving praise at school and in their home. Therefore, some of them could feel offended when the teacher pointed out the errors they made. The teacher explained that some of these students felt that teacher’s comments about their mistakes can cast doubt on their status of smart students.

I will make my dreams come true

We observed students who showed deep engagement and concentration on the task and who were driven by the desire to achieve their long-term goals. For example, B16was a male student who showed exceptional engagement and concentration during the projects. In the theoretical part, he asked questions to be sure he understood everything. In the practical lessons, he tried to dedicate extra time to the project and always wanted to go a step further than the rest of the students. He helped students who asked for it but rejected attempts to distract him from the learning. On his own initiative, B16 changed one of his major subjects from computer science to engineering systems. His concentration on and level of immersion in the task were outstanding. He did not express excitement but looked motivated and immersed in the assignments. The level of his learning engagement was higher than that described in literature by the engagement structure “I’m really into this”.

The teacher commented that this student was one of the best students in the group. The teacher knew that the student was good in computer science, and he was honoured to accept him in the group when B16 asked his permission. The teacher said this student was outstanding in his group for his creativity and the level of learning engagement. The teacher also noted the student was very interested in the subject because he decided to join the engineering systems course even though in computer science, he would receive a higher bonus. Moreover, the teacher commented that one of the reasons for this learning behaviour was that his parents instilled him the desire to learn and strongly influenced his excellent behaviour.

In the interview, the student explained that he moved from computer science to engineering systems because in computer science, it was no chance to build something with a 3D printer, actuators, etc. While in engineering systems, he has this chance as well as the opportunity of programming system controllers. Regarding his learning behaviour, B16 explained that he behaved the same way in all the subjects and wanted to be successful in all his learning activities. Also, at home, his parents taught him to respect and value the teacher’s work by respecting the teacher and do his best on each assignment. The students said he wanted to be a pilot in the future.

My way or no way at all

The researchers observed episodes when the students were only engaged when they solved problems and carried out tasks in their own way. If that worked, they continued to be engaged in the task; but if not, they stopped and became disengaged. For instance, B12 was a male student who had worked on a project and who had always dedicated a great amount of time to his assignments. However, the current project had to be developed in his way. If he faced a problem, he tried to find a solution on the Internet. If he could not find the solution, he looked for a peer who could help him. If he did not get help from peers he became disengaged and started to talk with friends and play games on the Internet. Similar cases were found in other students.

The teacher commented that through his experiences he has realized that some of the students are accustomed to certain learning strategies, and their success and failure in applying these strategies largely determines their learning engagement. The teacher noticed that he experienced difficulties in trying to change their learning strategies.

This section begins with a summary of our findings, directly addressing our research questions. We continue by discussing the engagement structures we had identified in relation to existing literature on student engagement, technology education, and learning with robots. We end with implications of our findings to robotics education, recommendations for educators and researchers, and a summary of the contribution of this study.

Summary of findings

The first research question was: ‘What engagement structures can be identified in high school students studying in a robotics course?’ In response to this question, we observed that our research sample (N = 41) exhibited 23 engagement structures in total, 12 of which were already known and existed in the literature, and 11 of which were novel (see Tables ​ Tables2, 2 , ​ ,3, 3 , ​ ,4 4 for details). Previous studies that investigated engagement structures in other disciplines had also identified novel structures (Goldin, 2018 ; Perez & Verner, 2019 ; Verner & Revzin, 2017 ; Verner et al., 2013 ).

The second research question was ‘what are the commonalities and differences between engagement structures of the two groups of students—those preparing for academic engineering programs and those preparing for technical college?’ In response to this question, we observed that School A group (n 1  = 20) exhibited 17 engagement structures, and that School B group (n 2  = 21) exhibited 15 structures. The two groups shared nine known structures, but no novel structures: the six novel structures identified in School A group were not observed in School B group, and the five novel structures identified in School B group were not observed in School A group. We identified all the engagement structures that had appeared in the literature. See Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4 4 for details.

We identified three known engagement structures that differentiated between the two groups, namely ‘Don’t disrespect me’ and ‘I don’t want to learn it’ (School A group), and ‘Look how smart I am’ (School B group). Moreover, the two groups of students did not share any novel structures with each other; while School A group’s novel structures centred around their difficulties with learning the topic at hand, School B group’s novel structures centred around taking charge, or wanting to take charge of their own learning. These structures represent different perceptions of the learning experience and of oneself as a learner, similar to those we found for the known structures which the two groups did not share.

The distinction we found in engagement structures—both novel and previously reported—between the two groups warrants explanation. In our opinion, this distinction is rooted in the significant differences in prior academic achievement between the two groups of students. We did not have access to the data on the students’ achievements, and our inference about these differences is based on the evaluation of their prior knowledge given by the teachers. The situation, in which the academic performance of school students in technical tracks is lower than that in academic tracks, is quite typical (Małgorzata et al., 2018 ). Since, as known (Dong et al., 2020 ; Lee, 2014 ; Lei et al., 2018 ; Piñeiro et al., 2019 ) that student academic achievement and learning engagement are in direct relationship, we explain the observed differences in learning engagement in the two robotics courses by the differences in the level of students’ prior academic achievement in the two groups.

Related to this explanation, Wu and Huang ( 2007 ) reported that students who were low achievers tended to be disengaged when compared with high achieving students, when the instructional method was student-centred. Their findings corroborated our characterization of the novel engagement structures we found in both study groups. The high achievers expressed wanting to direct their own study (‘Look how smart I am’), whereas students in the lower achieving group expressed desire for further instructional support from the teacher. In the specific case of learning with robots, a potential compounding factor for low achieving students’ disengagement (‘Don’t disrespect me’, ‘I don’t want to learn it’) could be their difficulties in learning the new subject through the experiential learning cycle (Verner and Korchnoy 2006 ), due to their lack of reflective thinking and conceptual learning skills.

Learning with robots has benefits which are unrelated to students’ academic level, as well as benefits which are related to it: collaborative learning in robotic environments can benefit high achieving students by developing their teamwork skills, which are essential for the current workforce (Ananiadou & Claro, 2009 ; Korchnoy & Verner, 2010 ; National Research Council, 2012 ; Verner & Hershko, 2003 ), and for lower achieving students, the hands-on interdisciplinary activities with robots provide an effective way to learn STEM concepts and acquire technical skills (Cuperman & Verner, 2013 ; Spolaôr & Benitti, 2017 ).

Implications and recommendations

Our findings show that the study of student engagement should involve learning processes in different educational tracks, and involve groups of students with different achievement levels, as each group may exhibit distinct patterns of engagement. Another implication of our findings is that monitoring students’ engagement is of particular importance for experiential learning in robotic environments, as it allows the teacher to gain a fuller understanding of the students, as individuals and as a group.

This study can serve as a basis for further investigations into student engagement in robotics, and in particular for exploring students of various achievement levels. Teasing out differences in prior knowledge and in learning skills would make for an important contribution to the understanding of how engagement structures vary between students with different academic achievement levels. Future research could also build on the present study and explore student groups in other educational frameworks and contexts, such as middle-school or informal education. Observing and analysing the change in individual students’ engagement structures over time, as Verner and Revzin ( 2017 ), would be another potentially fruitful avenue of research.

The authors can make four recommendations for robotics teachers interested in improving their students’ learning engagement:

• Identify each student’s internal motivations and use this understanding to facilitate students’ autonomy;
• Monitor students’ classroom engagement: observe whether or not a student is paying attention, putting forth effort, enjoying class, exploring problems and solutions, and contributing constructively to classroom discussions;
• Employ different strategies for introducing students to robotic technologies and concepts, thereby creating different entry points for engaging students with diverse interests and learning styles; and
• For a given instructional scenario, ascertain the STEM knowledge and skills required for experiential learning with robots, and determine whether students have these knowledge and skills before deciding which instructional approach to apply.

Contribution

Whereas in most studies on student engagement, observations are conducted over a few months or less, this study involved an entire year of observations. This kind of systematic, long-term observation provides a more reliable portrayal of learning engagement, producing findings and conclusions which add to the theory of engagement structures. Another distinction of this study lies in being one of the first studies on high school robotics student engagement. The study contributes to understanding of student engagement in learning robotics for two different categories of high school students: low achieving students majoring in technology education, and high achieving students who study computer science, a natural science subject, and mechatronics.

The findings and conclusions of this study, and in particular the novel engagement structures and the distinctions that were identified between student achievement groups, can provide educators with a better understanding of their students’ needs and behaviours. This study can help teachers identify learning opportunities and more precisely tailor teaching robotics to their students. Additionally, the concept of engagement structures, along with those identified and explained herein, can be included in teacher education and in professional development programs.

With learning increasingly taking place in technology-rich environments, and being conducted remotely and online, the investigation of student engagement is becoming even more important than before. This is highly pertinent for robotics education, where physical hands-on activities with technological systems are the norm. Currently, a quick Google Scholar search for “robotics education” engagement reveals about 35,000 results, compared with, for example, “mathematics education” engagement , with more than 1,300,000 results. This indicates that there is still a large scope for investigation of student engagement in robotics education. We hope that this study will serve as a basis for future studies that will expand upon this topic.

Declarations

Not applicable.

In order to carry out the study, the authors applied and obtained permission from the Behavioral Sciences Research Ethics Committee of the University and the Chief scientist of the Israel Ministry of Education.

The goal and method of our study was explained to the participating students in advance. The participants of this study were students from two high schools. They had the right to cease their participation and exit the study with no ramifications or consequences. The participants' identities were kept anonymous for all our publications; all names were changed and there is no information was disclosed that can be used to recognize any specific individual. The researcher made in-class observations with the consent of the schools’ teachers and with the permission of the schools’ principals.

All the authors of the manuscript agreed with its content, gave explicit consent to submit it, and obtained consent from the responsible authorities at the University where the work has been carried out.

Publisher's Note

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

• Alimisis D. Educational robotics: Open questions and new challenges. Themes in Science and Technology Education. 2013; 6 (1):63–71. [ Google Scholar ]
• Alonso-Nuez, M. J., Gil-Lacruz, A. I., & Rosell-Martínez, J. (2020). Assessing evaluation: Why student engages or resists to active learning? International Journal of Technology and Design Education, 1–17.
• Ananiadou, K., & Claro, M. (2009). 21st century skills and competences for new millennium learners in OECD countries.
• Anwar S, Bascou NA, Menekse M, Kardgar A. A systematic review of studies on educational robotics. Journal of Pre-College Engineering Education Research. 2019; 9 (2):2. doi: 10.7771/2157-9288.1223. [ CrossRef ] [ Google Scholar ]
• Armier DD, Jr, Shepherd CE, Skrabut S. Using game elements to increase student engagement in course assignments. College Teaching. 2016; 64 (2):64–72. doi: 10.1080/87567555.2015.1094439. [ CrossRef ] [ Google Scholar ]
• Barak M. Motivating self-regulated learning in technology education. International Journal of Technology and Design Education. 2010; 20 (4):381–401. doi: 10.1007/s10798-009-9092-x. [ CrossRef ] [ Google Scholar ]
• Benitti FBV. Exploring the educational potential of robotics in schools: A systematic review. Computers & Education. 2012; 58 (3):978–988. doi: 10.1016/j.compedu.2011.10.006. [ CrossRef ] [ Google Scholar ]
• Blumenfeld PC, Soloway E, Marx RW, Krajcik JS, Guzdial M, Palincsar A. Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist. 1991; 26 (3–4):369–398. doi: 10.1080/00461520.1991.9653139. [ CrossRef ] [ Google Scholar ]
• Braun Virginia, Clarke Victoria. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3 (2):77–101. doi: 10.1191/1478088706qp063oa. [ CrossRef ] [ Google Scholar ]
• Brown, L., Kerwin, R., & Howard, A. M. (2013). Applying behavioral strategies for student engagement using a robotic educational agent. In 2013 IEEE international conference on systems, man, and cybernetics (pp. 4360–4365).
• Church, W. J., Ford, T., Perova, N., & Rogers, C. (2010). Physics with robotics—using LEGO MINDSTORMS in high school education. In 2010 AAAI Spring symposium series .
• Code, J., Ralph, R., & Forde, K. (2020). Pandemic designs for the future: Perspectives of technology education teachers during COVID-19. Information and Learning Sciences .
• Craft AM, Capraro RM. Science, technology, engineering, and mathematics project-based learning: Merging rigor and relevance to increase student engagement. Electronic International Journal of Education, Arts, and Science. 2017; 3 (6):142–158. [ Google Scholar ]
• Creswell JW, Hanson WE, Clark Plano VL, Morales A. Qualitative research designs: Selection and implementation. The Counseling Psychologist. 2007; 35 (2):236–264. doi: 10.1177/0011000006287390. [ CrossRef ] [ Google Scholar ]
• Cross, J. L., Hamner, E., Bartley, C., & Nourbakhsh, I. (2015). Arts & Bots: Application and outcomes of a secondary school robotics program. In 2015 IEEE frontiers in education conference (FIE) (pp. 1–9). IEEE.
• Cuperman D, Verner IM. Learning through creating robotic models of biological systems. International Journal of Technology and Design Education. 2013; 23 (4):849–866. doi: 10.1007/s10798-013-9235-y. [ CrossRef ] [ Google Scholar ]
• Dong A, Jong MSY, King RB. How does prior knowledge influence learning Engagement? The mediating roles of cognitive load and help-seeking. Frontiers in Psychology. 2020; 11 :1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Goldin, G. A. (2018). Discrete mathematics and the affective dimension of mathematical learning and engagement. In Teaching and learning discrete mathematics worldwide: Curriculum and research (pp. 53–65). Springer, Cham.
• Goldin GA, Epstein YM, Schorr RY, Warner LB. Beliefs and engagement structures: Behind the affective dimension of mathematical learning. ZDM. 2011; 43 (4):547. doi: 10.1007/s11858-011-0348-z. [ CrossRef ] [ Google Scholar ]
• Green, J. L., Camilli, G., & Elmore, P. B. (Eds.). (2012). Handbook of complementary methods in education research . Routledge.
• Han, S., & Bhattacharya, K. (2001). Constructionism. Learning by design, and project-based learning. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology . Retrieved June, 15, 2008.
• Harel IE, Papert SE. Constructionism. Ablex Publishing; 1991. [ Google Scholar ]
• Hashimoto, T., Kobayashi, H., Polishuk, A., & Verner, I. (2013). Elementary science lesson delivered by robot. In 2013 8th ACM/IEEE international conference on human-robot interaction (HRI) (pp. 133–134). IEEE.
• Hatch, J. A. (2002). Doing qualitative research in education settings. Suny Press. Jensen, E. (2013). How poverty affects classroom engagement. Educational Leadership, 70 (8), 24–30.
• Jensen, E. (2013). How poverty affects classroom engagement. Educational Leadership, 70 (8), 24–30.
• Jones, B. F., Rasmussen, C. M., & Moffitt, M. C. (1997). Real-life problem solving: A collaborative approach to interdisciplinary learning . American Psychological Association.
• Kaburlasos, V. G., & Vrochidou, E. (2019). Social robots for pedagogical rehabilitation: Trends and novel modeling principles. In Cyber-physical systems for social applications (pp. 1–21). IGI global.
• Kalina C, Powell KC. Cognitive and social constructivism: Developing tools for an effective classroom. Education. 2009; 130 (2):241–250. [ Google Scholar ]
• Kelly S, Price H. Vocational education: A clean slate for disengaged students? Social Science Research. 2009; 38 (4):810–825. doi: 10.1016/j.ssresearch.2009.05.002. [ CrossRef ] [ Google Scholar ]
• Khalil, D., Lake, E., & Johnson, A. (2019). Teachers’ classroom engagement structures: A comparative study of a novice US and an experienced UK mathematics teacher. In Affect and mathematics education (pp. 255–282). Springer, Cham.
• Korchnoy E, Verner IM. Characteristics of learning computer-controlled mechanisms by teachers and students in a common laboratory environment. International Journal of Technology and Design Education. 2010; 20 (2):217–237. doi: 10.1007/s10798-008-9071-7. [ CrossRef ] [ Google Scholar ]
• Lee E, Hannafin MJ. A design framework for enhancing engagement in student-centered learning: Own it, learn it, and share it. Educational Technology Research and Development. 2016; 64 (4):707–734. doi: 10.1007/s11423-015-9422-5. [ CrossRef ] [ Google Scholar ]
• Lee JS. The relationship between student engagement and academic performance: Is it a myth or reality? The Journal of Educational Research. 2014; 107 (3):177–185. doi: 10.1080/00220671.2013.807491. [ CrossRef ] [ Google Scholar ]
• Lei H, Cui Y, Zhou W. Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: An International Journal. 2018; 46 (3):517–528. doi: 10.2224/sbp.7054. [ CrossRef ] [ Google Scholar ]
• Liu CC, Chen I. Evolution of constructivism. Contemporary Issues in Education Research. 2010; 3 (4):63–66. [ Google Scholar ]
• Małgorzata, K., Tanja, B., & Simon, F. (2018). OECD reviews of vocational education and training apprenticeship and vocational education and training in Israel . OECD Publishing.
• National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century . National Academies Press.
• Newmann, F. M. (1992). Student engagement and achievement in American secondary schoo ls. Teachers College Press, 1234 Amsterdam Avenue, New York, NY 10027 (paperback: ISBN-0-8077-3182-X, $17.95; hardcover: ISBN-0-8077-3183-8, $38).
• Papert S. Mindstonns. Basic Rooks; 1980. p. 607. [ Google Scholar ]
• Perez, H., & Verner, I. (2019). Engagement in learning through design and experimentation with robots. In International conference on interactive collaborative learning (pp. 297–305). Springer, Cham.
• Piñeiro I, Estévez I, Freire C, de Caso A, Souto A, González-Sanmamed M. The role of prior achievement as an antecedent to student homework engagement. Frontiers in Psychology. 2019; 10 (140):1–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reeve, J. (2012). A self-determination theory perspective on student engagement. In Handbook of research on student engagement (pp. 149–172). Springer, Boston, MA.
• Reimers, F. M., & Schleicher, A. (2020). A framework to guide an education response to the COVID-19 Pandemic of 2020. OECD. Retrieved April, 14, 2020.
• Reynolds R, Caperton IH. Contrasts in student engagement, meaning-making, dislikes, and challenges in a discovery-based program of game design learning. Educational Technology Research and Development. 2011; 59 (2):267–289. doi: 10.1007/s11423-011-9191-8. [ CrossRef ] [ Google Scholar ]
• Rihtaršič D, Avsec S, Kocijancic S. Experiential learning of electronics subject matter in middle school robotics courses. International Journal of Technology and Design Education. 2016; 26 (2):205–224. doi: 10.1007/s10798-015-9310-7. [ CrossRef ] [ Google Scholar ]
• Rusk N, Resnick M, Berg R, Pezalla-Granlund M. New pathways into robotics: Strategies for broadening participation. Journal of Science Education and Technology. 2008; 17 (1):59–69. doi: 10.1007/s10956-007-9082-2. [ CrossRef ] [ Google Scholar ]
• Schreiber LM, Valle BE. Social constructivist teaching strategies in the small group classroom. Small Group Research. 2013; 44 (4):395–411. doi: 10.1177/1046496413488422. [ CrossRef ] [ Google Scholar ]
• Scruggs B. Constructivist practices to increase student engagement in the orchestra classroom. Music Educators Journal. 2009; 95 (4):53–59. doi: 10.1177/0027432109335468. [ CrossRef ] [ Google Scholar ]
• Spolaôr N, Benitti FBV. Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education. 2017; 112 :97–107. doi: 10.1016/j.compedu.2017.05.001. [ CrossRef ] [ Google Scholar ]
• Trowler V. Student engagement literature review. The Higher Education Academy. 2010; 11 (1):1–15. [ Google Scholar ]
• Verner, I. (2013). Characteristics of student engagement in robotics. In FIRA RoboWorld congress (pp. 181–194). Springer, Berlin, Heidelberg.
• Verner I, Korchnoy E. Experiential learning through designing robots and motion behaviors: A tiered approach. International Journal of Engineering Education. 2006; 22 (4):758–765. [ Google Scholar ]
• Verner, I. M., & Hershko, E. (2003). School graduation project in robot design: A case study of team learning experiences and outcomes . 14, 2 (spring 2003).
• Verner I, Massarwe K, Bshouty D. Constructs of engagement emerging in an ethnomathematically-based teacher education course. The Journal of Mathematical Behavior. 2013; 32 (3):494–507. doi: 10.1016/j.jmathb.2013.06.002. [ CrossRef ] [ Google Scholar ]
• Verner, I. M., & Revzin, L. B. (2017). Robotics in school chemistry laboratories. In Robotics in education (pp. 127–136). Springer, Cham.
• Wu HK, Huang YL. Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education. 2007; 91 (5):727–749. doi: 10.1002/sce.20216. [ CrossRef ] [ Google Scholar ]
• You, Z. J., Shen, C. Y., Chang, C. W., Liu, B. J., & Chen, G. D. (2006). A robot as a teaching assistant in an English class. In Sixth IEEE international conference on advanced learning technologies (ICALT'06) (pp. 87–91).

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10 Robotics Programs for High School Students

With robots becoming more prominent in our society, the field of robotics will only continue to advance in the future. If you are interested in building and designing machines, then this career path may be for you.

Most of these summer programs will involve not only learning about the field of engineering as a whole but applying these concepts to make creations of your own.

Here are 10 robotics opportunities available to ambitious high school students.

1. veritas ai's summer fellowship program.

Veritas AI has a range of   AI programs for ambitious high school students , starting from close-group, collaborative learning to customized project pathways with 1:1 mentorship . The programs have been designed and run by Harvard graduate students & alumni.

In the AI Fellowship, students create a novel AI project independently with the support of a mentor over 12-15 weeks. Many students also combine AI with robotics. Examples of past projects can be found here .

Location:   Virtual

Fee: $1,490 for the AI Scholars program (A 10-week bootcamp). $4,200 for the AI Fellowship (the 12-15 week 1-1 mentorship). $4,700 for both. Need-based financial aid is available.

Application deadline: Program runs in cohorts throughout the year - applications this year will happen on March 1, May 1, September 1, and December 1 (as of writing) .

Program dates: Applications for the summer are between February and May.

Program selectivity: Moderate (AI Fellowship)

Eligibility: Applicants must be ambitious, high school and can be anywhere in the world. For the AI Scholars program, no previous experience is required - applicants need to show a keen interest in AI. For the AI Fellowship program, applicants will either need to complete the AI Scholars program or have had past experience with AI concepts or Python.

2. MIT Lincoln Laboratory Radar Introduction for Student Engineers

If you are specifically interested in radar systems, then MIT’s LLRISE program will be suited for you. Throughout July, rising seniors will be taught how to build small radar systems, such as a Doppler and range radar. Highly talented scientists and engineers will be working alongside the attendees and assisting them. The workshop will be held at two locations: the MIT campus in Cambridge, MA and Lincoln Laboratory in Lexington, MA.

Costs: None

Eligibility:

Be a U.S. citizen. Foreign citizens who are permanent residents are not eligible.

Be passionate about STEM.

Be a current junior.

Must be fully vaccinated against COVID-19 or have a religious or medical exemption.

Selectivity: Very High

3. Northeastern Young Scholar’s Program (YSP)

This program has an emphasis on providing hands-on research experience for Massachusetts students who are rising seniors. Therefore, attendees can expect to work with experienced Northeastern faculty at the research laboratories within Northeastern University’s Colleges of Engineering, Science, and Health Sciences, which can range from bioengineering and artificial intelligence – although this year’s projects are not yet fully decided. The program also offers education and career counseling, a chance to explore college life, field trips, and career exploration seminar series on topics including engineering.

Must be permanent Massachusetts residents.

Priority access is given to those students who have low access to similar programs and live within commuting distance of Northeastern University.

Students in Maine are eligible to participate in Young Scholars’ Program @ Roux Institute in Portland, Maine. More details found here: https://www.surveymonkey.com/r/2023-YSPR-APPLY .

Must be a current junior student.

Students from any school (public, private, homeschooled, etc) are eligible.

Selectivity: High

4. Lumiere Research Scholar Program – Robotics Track

Lumiere has been founded by researchers at Harvard and Oxford. Hundreds of ambitious high school students do politics research through the Lumiere Research Scholar Programs. Each student is paired with a top PhD and works with their mentor 1-on-1 to produce a university-level research paper.

The programs are fully virtual and vary in duration based on the student’s end goal with respect to how much of a deep dive they would prefer. The research opportunities range from pure political science to combining politics with other social sciences.

Past research projects include comparisons in sports training between humans and robots, exploring how robots increase the efficacy of monitoring disasters such as wildfires

Also, check out the Lumiere Foundation , a non-profit research program for talented, low-income students.

Location: Virtual

Application deadline: There are four cohorts throughout the year. Applications are due in February, May, September, and December, respectively. Apply here !

Program dates: There are four cohorts throughout the year in spring, summer, fall, and winter.

Eligibility: All high school students may apply.

5. UT Austin Academy for Robotics

The UT Computer Science Summer Academies is offering high schoolers of all skill levels the opportunity to learn more about robotics on the University of Texas campus. The topics that will be covered include but are not limited to introductory programming, Arduino, sensors, and controlling a robot. As such, the hands-on experience will involve assembling and programming a robot, utilizing tools in robotics research, competing in a robot race, and more. Of course, you can expect to explore the UT campus and meet current students as well. Applications will open January 31st.

Costs: $2,100, limited scholarships available

Current 9th - 12th grade students

6. University of Wisconsin Madison Engineering Summer Program

For rising 9th and 10th graders, the Virtual Engineering Summer Program will be held over a course of a week. Not only are there workshops for students to learn about engineering majors at UW Madison, but also they will be mailed engineering kits and have hands-on experience in robotics design. On the other hand, for rising 11th and 12th graders, the residential Engineering Summer Program will be held over the course of three weeks. The program offers a curriculum in STEM, hands-on workshops, visits to industry sites, field trips, mentoring from faculty, and an opportunity to network with experts in the industry. Students will be expected to learn about design and work in teams.

Have an interest in STEM

Be a U.S. citizen or permanent resident

Be a current 8th grader or freshman in high school

Have a strong interest in STEM

Be a current sophomore or junior in high school

Have completed at least one year of algebra, geometry, and chemistry

Have a minimum unweighted grade-point average of 3.0 on a 4.0 scale.

7. Tufts Engineering Design Lab

This program emphasizes engineering, fabrication, robotics, and computation in the context of solving real world problems. Those selected for the program will be allowed to pursue their own engineering design projects through the state-of-the-art Nolop Makerspace at Tufts’s technology, which includes laser cutting, 3D printing, robotics, and computation. Guest speakers will talk about their work and the importance of engineering, which may serve as inspiration for the student’s projects of choice.

Costs: Commuter: $4,000, Residential: $5,500, Materials Fee: $200, limited scholarships available

Currently in grades 10-12 or graduating from high school in spring 2022.

For residential students,they must be no younger than 15 at the start of the program and no older than 17 before the program end date.

Both domestic and international students are welcome to apply, but if English is not your primary language you will need to submit evidence of English Language proficiency.

8. MIT Introduction to Technology, Engineering, and Science (MITES)

For 6 weeks, high school students will spend seven hours per workday in classes or other activities, which can range from STEM and humanities courses, workshops, guest speakers, and tours. One of these classes will be an elective that will require the student to work on work on a project, and past elective subjects have included engineering design and machine learning. At the end of the program, there will be a final symposium for these projects to be presented to the broader MIT community.

U.S. citizens or permanent residents

Be a current high school junior

9. Embry–Riddle Aeronautical University Robotics & Autonomous Systems Camp

As the name of the program implies, this Embry-Riddle summer program will focus on robotics & autonomous systems, which is a rapidly expanding field. With the Embry-Riddle faculty and undergraduate students in robotics competition teams, program attendees will create and test autonomous robots of their own. Throughout the week, students will be incorporating concepts from mechanical, electrical, and computer engineering, which will be useful in their future careers in engineering and robotics.

Costs: $975

Eligibility: Students aged 15-18

Selectivity: Moderate

10. Saint Louis University Robotics Summer Academy

During Saint Louis University's Robotics Summer Academy, attendees will establish teams and build their own robots using custom hardware. This project will involve mechanical, electrical and computer engineering concepts in order to design and fabricate robot parts. These robots will be put into friendly competitions with other students’ robots, and at the end of camp, students will have their own functioning robot to keep. Moreover, participants will meet with current SLU engineering students and faculty, visit their engineering labs and facilities, and explore campus.

This is a day camp only; overnight housing is not provided for the Robotics Summer Academy.

Costs: $650, scholarships available

High school students entering grades 9 through 12.

Space is limited to 25 students.

Bonus option : Worcester Polytechnic Institute Frontiers

WPI’s residential program provides two weeks of STEM exploration in their course curriculum. Uniquely, while students will primarily focus on a STEM “major,” they also will pursue a humanities “minor,” which can range from psychology to painting to business. If you choose Robotics Engineering as your major, you will learn about robotics engineering, such as force, torque, and stress analysis, material properties, processing, and selection, power requirements, micro controllers, sensor operations, programming, pneumatics. With this knowledge you will create a machine to solve a challenging robotics problem and be tested in an end-of-session tournament.

Cost: $3,595, financial assistance available

Be a rising 10, 11, and 12th grade

Bonus - Ladder Internship Program

Ladder Internships is a selective program equipping students with virtual internship experiences at startups and nonprofits around the world!  The startups range across a variety of industries, and each student can select which field they would most love to deep dive into. This is also a great opportunity for students to explore areas they think they might be interested in, and better understand professional career opportunities in those areas. The startups are based all across the world, with the majority being in the United States, Asia and then Europe and the UK. 

The fields include technology, machine learning and AI, finance, environmental science and sustainability, business and marketing, healthcare and medicine, media and journalism and more.  

You can explore all the options here on their application form . As part of their internship, each student will work on a real-world project that is of genuine need to the startup they are working with, and present their work at the end of their internship. In addition to working closely with their manager from the startup, each intern will also work with a Ladder Coach throughout their internship - the Ladder Coach serves as a second mentor and a sounding board, guiding you through the internship and helping you navigate the startup environment. 

Interns are offered one-on-one training in communication, time management and other such valuable skills and will also have the opportunity to attend group training sessions with other interns in their cohort. The virtual internship is usually 8 weeks long.

Cost : $1490 (Financial Aid Available)

Location:   Remote! You can work from anywhere in the world.

Application deadline:  April 16 and May 14

Program dates:  8 weeks, June to August

Eligibility: Students who can work for 10-20 hours/week, for 8-12 weeks. Open to high school students, undergraduates and gap year students!

One other option – Lumiere Research Scholar Program

If you are passionate about research, you could also consider applying to the Lumiere Research Scholar Program , a selective online high school program for students that was founded by researchers at Harvard and Oxford. Last year, we had over 2100 students apply for 500 spots in the program! You can find the application form here.

Lydia is currently a sophomore at Harvard University, studying Molecular and Cellular Biology. During high school, she pursued engineering activities like attending the Governor's School of Engineering and Technology. In her spare time, she likes to create digital art while listening to music.

Commentaires

Perception of High School Students on the Role of Robotics as a Tool for 21st Century Learning

• Jeremiah Kobe M. Aureada
• Angelo Antonio C. Dalusong
• Jeiram Marje M. Gonzales
• Johan Mari C. Ocampo
• Axel Joshua T. Pagayon

Schools in the Philippines are now implementing robotics subject in their K-12 Curriculum for the students to learn the concepts of programming robots, including St Mary's College Quezon City which started having robotics in SY 2016-2017. Thus, it will be significant to find out the students' perceptions on robotics as a tool for learning. The researchers will use phenomenology as their research design. The research includes purposively chosen informants who have varied experiences in robotics. Three (3) informants have competed in a robotics competition. Three (3) informants have taken the subject but have not competed in a robotics competition. One (1) informant is a teacher of the Robotics subject. A semi-structured interview guide was used to gather relevant data. The informants deem that they had acquired these 21st century skills: a) Critical Thinking; b) Collaboration; c) Creativity; d) Technology Literacy; e) Leadership; f) Social Skills; g) Communication; h) Productivity, through robotics classes. The informants also believe that robotics enhanced their 21st century skills because they are able to use the skills that they develop and acquire in robotics in their daily lives. Struggles include the limited slots in the robotics training program -only for students who excel and meet the standards of having a gold medal in robotics from the previous year. Thus, other students are not able to fully experience the beauty of robotics. The Robotics program was the school’s innovative program to make sure that students are at part with global standards. Based on their feedback, Robotics has helped them improve their 21st century skills and learned something they can apply in their daily lives. However, the school should also consider offering the program to interested students who would like to have the same opportunities as those who have higher skills in robotics.

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These 81 robotics companies are hiring

When I attended Automate in Chicago a few weeks back, multiple people thanked me for TechCrunch’s semi-regular robotics job report. It’s always edifying to get that feedback in person.

While it’s true that the industry has seen ups and downs in terms of both funding and hiring in recent years, there’s never been a more exciting time to be in robotics. Whether it’s established categories like manufacturing and fulfillment or emerging verticals like humanoids and home robotics, things are moving faster than ever.

What strikes me the most when compiling these lists, however, is not just the fact that there are more openings every time. It’s also the breadth of categories that robotics currently touches. It’s a great time to be involved in the space, because in the near future, robotics is going to impact every aspect of our lives.

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E-Cigarette Use Among Youth

What to know.

E-cigarettes are the most commonly used tobacco product among U.S. youth. No tobacco products, including e-cigarettes, are safe, especially for children, teens, and young adults. Learn more about e-cigarette use among youth.

• In the United States, youth use e-cigarettes, or vapes, more than any other tobacco product. 1
• No tobacco products, including e-cigarettes, are safe, especially for children, teens, and young adults. 2
• Most e-cigarettes contain nicotine, which is highly addictive. Nicotine can harm the parts of an adolescent's brain that control attention, learning, mood, and impulse control. 2
• E-cigarette marketing, the availability of flavored products, social influences, and the effects of nicotine can influence youth to start or continue vaping. 3 4
• Most middle and high school students who vape want to quit. 5
• Many people have an important role in protecting youth from vaping including parents and caregivers, educators and school administrators, health care providers, and community partners.
• States and local communities can implement evidence-based policies, programs, and services to reduce youth vaping.

E-cigarette use among U.S. youth

In 2023, e-cigarettes were the most commonly used tobacco product among middle and high school students in the United States. In 2023: 6

• 550,000 (4.6%) middle school students.
• 1.56 million (10.0%) high school students.
• Among students who had ever used e-cigarettes, 46.7% reported current e-cigarette use.
• 1 in 4 (25.2%) used an e-cigarette every day.
• 1 in 3 (34.7%) used an e-cigarette on at least 20 of the last 30 days.
• 9 in 10 (89.4%) used flavored e-cigarettes.
• Most often used disposable e-cigarettes (60.7%) followed by e-cigarettes with prefilled or refillable pods or cartridges (16.1%).
• Most commonly reported using the following brands: Elf Bar, Esco Bars, Vuse, JUUL, and Mr. Fog.

Most middle and high school students who vape want to quit and have tried to quit. 5 In 2020:

• 63.9% of students who currently used e-cigarettes reported wanting to quit.
• 67.4% of students who currently used e-cigarettes reported trying to quit in the last year.

Most tobacco use, including vaping, starts and is established during adolescence. There are many factors associated with youth tobacco product use . These include:

• Tobacco advertising that targets youth.
• Product accessibility.
• Availability of flavored products.
• Social influences.
• Adolescent brain sensitivity to nicotine.

Some groups of middle and high school students use e-cigarettes at a higher percentage than others. For example, in 2023: 6

• More females than males reported current e-cigarette use.
• Non-Hispanic multiracial students: 20.8%.
• Non-Hispanic White students: 18.4%.
• Hispanic or Latino students: 18.2%.
• Non-Hispanic American Indian and Alaska Native students: 15.4%.
• Non-Hispanic Black or African American students: 12.9%.

Many young people who vape also use other tobacco products, including cigarettes and cigars. 7 This is called dual use. In 2020: 8

• About one in three high school students (36.8%) who vaped also used other tobacco products.
• One in two middle school students (49.0%) who vaped also used other tobacco products.

E-cigarettes can also be used to deliver other substances, including cannabis. In 2016, nearly one in three (30.6%) of U.S. middle and high school students who had ever used an e-cigarette reported using marijuana in the device. 9

• Park-Lee E, Ren C, Cooper M, Cornelius M, Jamal A, Cullen KA. Tobacco product use among middle and high school students—United States, 2022 . MMWR Morb Mortal Wkly Rep. 2022;71:1429–1435.
• U.S. Department of Health and Human Services. E-cigarette Use Among Youth and Young Adults: A Report of the Surgeon General . Centers for Disease Control and Prevention; 2016. Accessed Feb 14, 2024.
• Apelberg BJ, Corey CG, Hoffman AC, et al. Symptoms of tobacco dependence among middle and high school tobacco users: results from the 2012 National Youth Tobacco Survey . Am J Prev Med. 2014;47(Suppl 1):S4–14.
• Gentzke AS, Wang TW, Cornelius M, et al. Tobacco product use and associated factors among middle and high school students—National Youth Tobacco Survey, United States, 2021 . MMWR Surveill Summ. 2022;71(No. SS-5):1–29.
• Zhang L, Gentzke A, Trivers KF, VanFrank B. Tobacco cessation behaviors among U.S. middle and high school students, 2020 . J Adolesc Health. 2022;70(1):147–154.
• Birdsey J, Cornelius M, Jamal A, et al. Tobacco product use among U.S. middle and high school students—National Youth Tobacco Survey, 2023 . MMWR Morb Mortal Wkly Rep. 2023;72:1173–1182.
• Wang TW, Gentzke AS, Creamer MR, et al. Tobacco product use and associated factors among middle and high school students—United States, 2019 . MMWR Surveill Summ. 2019;68(No. SS-12):1–22.
• Wang TW, Gentzke AS, Neff LJ, et al. Characteristics of e-cigarette use behaviors among US youth, 2020 . JAMA Netw Open. 2021;4(6):e2111336.
• Trivers KF, Phillips E, Gentzke AS, Tynan MA, Neff LJ. Prevalence of cannabis use in electronic cigarettes among U.S. youth . JAMA Pediatr. 2018;172(11):1097–1099.

Smoking and Tobacco Use

Commercial tobacco use is the leading cause of preventable disease, disability, and death in the United States.

For Everyone

Health care providers, public health.