International Handbook of Metacognition and Learning Technologies

Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses

Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.001 · Cited by 1,003

Explainable Artificial Intelligence in education

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863

Overconfidence produces underachievement: Inaccurate self evaluations undermine students’ learning and retention

Dunlosky, J., & Rawson, K. A. (2012) · Learning and Instruction · doi:10.1016/j.learninstruc.2011.08.003 · Cited by 713

Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues

Azevedo, R. (2015) · Educational Psychologist · doi:10.1080/00461520.2015.1004069 · Cited by 418

The evolution of cognitive load theory and its application to medical education

Leppink, J., & Van den Heuvel, A. (2015) · Perspectives on Medical Education · doi:10.1007/s40037-015-0192-x · Cited by 299

How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement?

Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.01.005 · Cited by 220

Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning

Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198

Supporting Groups’ Emotion and Motivation Regulation during Collaborative Learning

Järvenoja, H., Järvelä, S., & Malmberg, J. (2020) · Learning and Instruction · doi:10.1016/j.learninstruc.2017.11.004 · Cited by 194

Short-and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance

Bannert, M., Sonnenberg, C., Mengelkamp, C., & Pieger, E. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.05.038 · Cited by 180

What multimodal data can tell us about the students’ regulation of their learning process?

Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.04.004 · Cited by 163

Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review

Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100138 · Cited by 152

Which one works best? Considering the relative importance of motivational regulation strategies

Schwinger, M., & Otterpohl, N. (2017) · Learning and Individual Differences · doi:10.1016/j.lindif.2016.12.003 · Cited by 111

Investigating prompts for supporting students' self-regulation – A remaining challenge for learning analytics approaches?

Schumacher, C., & Ifenthaler, D. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100791 · Cited by 109

Examining the use of prompts to facilitate self-regulated learning in Massive Open Online Courses

Wong, J., Baars, M., de Koning, B. B., & Paas, F. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106596 · Cited by 88

A feedback model for data-rich learning experiences

Pardo, A. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1356905 · Cited by 82

Towards strengthening links between learning analytics and assessment: Challenges and potentials of a promising new bond

Gašević, D., Greiff, S., & Shaffer, D. W. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107304 · Cited by 82

Using AI to Teach AI: Lessons from an Online AI Class

Goel, A. K., & Joyner, D. A. (2017) · AI Magazine · doi:10.1609/aimag.v38i2.2732 · Cited by 75

Integrating AI and machine learning in STEM education: Challenges and opportunities

Joseph, O. B., & Uzondu, N. C. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i8.1379 · Cited by 64

The Effectiveness of Pedagogical Agents’ Prompting and Feedback in Facilitating Co-adapted Learning with MetaTutor

Azevedo, R., Landis, R. S., Feyzi-Behnagh, R., Duffy, M., Trevors, G., Harley, J. M., Bouchet, F., Burlison, J., Taub, M., Pacampara, N., et al. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-30950-2_27 · Cited by 58

Do Instrumentation Tools Capture Self-Regulated Learning?

van der Graaf, J., Lim, L., Fan, Y., Kilgour, J., Moore, J., Bannert, M., Gasevic, D., & Molenaar, I. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448181 · Cited by 44

Reflections on the Ecolab and the Zone of Proximal Development

Luckin, R., & du Boulay, B. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0072-x · Cited by 16

Enhancing peer feedback provision through user interface scaffolding: A comparative examination of scripting and self-monitoring techniques

Lahza, H. F., Demartini, G., Noroozi, O., Gašević, D., Sadiq, S., & Khosravi, H. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105260 · Cited by 16

Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial

Fütterer, T., Bardach, L., Kuhn, J., Keller, S. D., & Gerjets, P. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10133-8 · Cited by 17