Biography
Roger Azevedo is a Pegasus Professor in the School of Modeling, Simulation and Training at the University of Central Florida. He is also an affiliated faculty in the Departments of Computer Science and Internal Medicine at the University of Central Florida and the lead scientist for the Learning Sciences Faculty Cluster. He received his Ph.D. in educational psychology from McGill University and completed his postdoctoral training in cognitive psychology at Carnegie Mellon University.
His main research areas include examining the role of cognitive, metacognitive, affective and motivational self-regulatory processes during learning with advanced learning technologies (e.g., intelligent tutoring systems, hypermedia, multimedia, simulations, serious games and immersive virtual learning environments). More specifically, his overarching research goal is to understand the complex interactions between humans and intelligent learning systems by using interdisciplinary methods to measure cognitive, metacognitive, emotional, motivational and social processes and their impact on learning, performance and transfer. To accomplish this goal, he conducts laboratory, classroom and in-situ (e.g., medical simulator) studies and collects multi-channel data to develop models of human-computer interaction; examines the nature of temporally unfolding self- and other-regulatory processes (e.g., human-human and human-artificial agents); and designs intelligent learning and training systems to detect, track, model and foster learners, teachers and trainers’ self-regulatory processes.
He has published over 300 peer-reviewed papers, chapters and refereed conference proceedings in the areas of educational, learning, cognitive, educational and computational sciences. He was the former editor of the Metacognition and Learning journal and serves on the editorial board of several top-tiered learning and cognitive sciences journals (e.g., Applied Cognitive Psychology, International Journal of AI in Education, Educational Psychology Review, European Journal of Psychological Assessment). His research is funded by the National Science Foundation (NSF), Institute of Education Sciences, National Institutes of Health, Social Sciences and Humanities Research Council of Canada, Natural and Sciences and Engineering Council of Canada, Canada Research Chairs, Canadian Foundation for Innovation, European Association for Research on Learning and Instruction, and the Jacobs Foundation. He is a fellow of the American Psychological Association and the recipient of the prestigious Early Faculty Career Award from the NSF.
His main research areas include examining the role of cognitive, metacognitive, affective and motivational self-regulatory processes during learning with advanced learning technologies (e.g., intelligent tutoring systems, hypermedia, multimedia, simulations, serious games and immersive virtual learning environments). More specifically, his overarching research goal is to understand the complex interactions between humans and intelligent learning systems by using interdisciplinary methods to measure cognitive, metacognitive, emotional, motivational and social processes and their impact on learning, performance and transfer. To accomplish this goal, he conducts laboratory, classroom and in-situ (e.g., medical simulator) studies and collects multi-channel data to develop models of human-computer interaction; examines the nature of temporally unfolding self- and other-regulatory processes (e.g., human-human and human-artificial agents); and designs intelligent learning and training systems to detect, track, model and foster learners, teachers and trainers’ self-regulatory processes.
He has published over 300 peer-reviewed papers, chapters and refereed conference proceedings in the areas of educational, learning, cognitive, educational and computational sciences. He was the former editor of the Metacognition and Learning journal and serves on the editorial board of several top-tiered learning and cognitive sciences journals (e.g., Applied Cognitive Psychology, International Journal of AI in Education, Educational Psychology Review, European Journal of Psychological Assessment). His research is funded by the National Science Foundation (NSF), Institute of Education Sciences, National Institutes of Health, Social Sciences and Humanities Research Council of Canada, Natural and Sciences and Engineering Council of Canada, Canada Research Chairs, Canadian Foundation for Innovation, European Association for Research on Learning and Instruction, and the Jacobs Foundation. He is a fellow of the American Psychological Association and the recipient of the prestigious Early Faculty Career Award from the NSF.
Recent Publications
Azevedo, R., Bouchet, F., Harley, J., Taub, M., Trevors, G., Cloude, E., Dever, D., Wiedbusch, M., Wortha, F., & Cerezo, R. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system. Frontiers in Psychology, 13:813632. doi: 10.3389/fpsyg.2022.813632
Azevedo, R., & Dever, D. (2022). Metacognition in multimedia learning. In R. E. Mayer & L. Fiorella (Eds.), Cambridge handbook of multimedia (3rd ed., pp. 132-141). Cambridge, MA: Cambridge University Press.
Azevedo, R., & Wiedbusch, M. (2023). Theories of metacognition and pedagogy applied in AIED systems. In du Boulay (Ed.), Handbook of Artificial Intelligence in Education (pp. 141-173). The Netherlands: Springer.
Cloude, E., Dever, D., Hahs-Vaughn, D., Emerson, A., Azevedo, R., & Lester, J. (2022). Affective dynamics and cognition during game-based learning. IEEE Transactions on Affective Computing, 13, 1705-1717.
Dever, D., Sonnenfeld, N., Wiedbusch, M., Schmorrow, S. G., Amon, M. J., Azevedo, R. (2023). A complex systems approach to analyzing pedagogical agents’ scaffolding of self‑regulated learning within an intelligent tutoring system. Metacognition & Learning. https://doi.org/10.1007/s11409-023-09346-x
Kovanovic, V., Azevedo, R., Gibson, D., & Ifenthaler, D. (Eds.) (2023). Unobtrusive observations of learning in digital environments: Examining behaviors, cognition, emotion, metacognition, and social processes using learning analytics. Springer.
Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gasevic, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel; data. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107540
Wiedbusch, M., Lester, J. & Azevedo, R. A multi-level growth modeling approach to measuring learner attention with metacognitive pedagogical agents. Metacognition & Learning 18, 465–494 (2023). https://doi.org/10.1007/s11409-023-09336-z
Azevedo, R., & Dever, D. (2022). Metacognition in multimedia learning. In R. E. Mayer & L. Fiorella (Eds.), Cambridge handbook of multimedia (3rd ed., pp. 132-141). Cambridge, MA: Cambridge University Press.
Azevedo, R., & Wiedbusch, M. (2023). Theories of metacognition and pedagogy applied in AIED systems. In du Boulay (Ed.), Handbook of Artificial Intelligence in Education (pp. 141-173). The Netherlands: Springer.
Cloude, E., Dever, D., Hahs-Vaughn, D., Emerson, A., Azevedo, R., & Lester, J. (2022). Affective dynamics and cognition during game-based learning. IEEE Transactions on Affective Computing, 13, 1705-1717.
Dever, D., Sonnenfeld, N., Wiedbusch, M., Schmorrow, S. G., Amon, M. J., Azevedo, R. (2023). A complex systems approach to analyzing pedagogical agents’ scaffolding of self‑regulated learning within an intelligent tutoring system. Metacognition & Learning. https://doi.org/10.1007/s11409-023-09346-x
Kovanovic, V., Azevedo, R., Gibson, D., & Ifenthaler, D. (Eds.) (2023). Unobtrusive observations of learning in digital environments: Examining behaviors, cognition, emotion, metacognition, and social processes using learning analytics. Springer.
Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gasevic, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel; data. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107540
Wiedbusch, M., Lester, J. & Azevedo, R. A multi-level growth modeling approach to measuring learner attention with metacognitive pedagogical agents. Metacognition & Learning 18, 465–494 (2023). https://doi.org/10.1007/s11409-023-09336-z
Education
- Postdoctoral Fellow, Carnegie Mellon University
- Ph.D., Educational Psychology, McGill University
- M.A., Educational Technology, Concordia University
- B.A., Psychology, Concordia University
Specialties
- Advanced learning and training technologies
- Human digital twins
- Human-machine AI collaboration
- Intelligent environments for education and training across humans and contexts
- Metacognition and self-regulated learning
- Multimodal process data in human-machine interactions