The SMART Lab focuses on understanding interactions between humans and intelligent learning/training systems by using advanced statistical methods to measure these processes and their impact on learning, training, performance, and transfer. We conduct basic and applied psychological research in several contexts, including laboratory and real-world environments (e.g., classrooms, medical simulators) using multimodal data, including eye movements, physiological sensors, verbalizations and discourse, human-machine interactions, log files, facial expressions of emotions, screen recordings, non-verbal behaviors, etc. These complex data are used to (1) develop theoretical, computational, and mathematical models of human-machine interaction; (2) examine the nature of temporally unfolding self- and other-regulatory processes (e.g., human-human and human-artificial agents); and (3) design adaptive intelligent learning and training systems capable of detecting, tracking, modeling, and fostering human and machine learning, intelligence, and performance across tasks, domains, and contexts to augment the knowledge, skills, and demands of the 21st-century workforce (e.g., empathy training in healthcare).
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. 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
Adaptive Intelligent Learning Systems Advanced Learning and Training Technologies Artificial Intelligence Digital Twins Human Digital Twins Human-human and Human-artificial agents 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
Capabilities & Advanced Technologies:
Eye-tracking Systems Intelligent Learning Systems Machine Learning Measurement Tools Multimodal Data Training Platforms