Self-regulated learning is an essential predictor of students’ learning, problem-solving, and reasoning across tasks, domains, and contexts. Cognitive, affective, metacognitive, and motivational processes play a crucial role in students’ ability to monitor and regulate their learning while using advanced learning technologies (ALTs) accurately, dynamically, and effectively. These processes are detected, modeled, inferred using multimodal data analytics, and used to provide intelligent, adaptive scaffolding and feedback learning while they use ALTs. However, despite advances by interdisciplinary researchers in multimodal data analytics, there remain many conceptual, theoretical, methodological, analytical, and pedagogical challenges and opportunities. This talk focuses primarily on: (1) providing a brief synthesis of the state-of-the-art of the field of multimodal data analytics (MLA); (2) presenting the challenges currently impacting the field of MLA (e.g., theoretical grounding, methodological rigor, inferences regarding complex interactions between self-regulated learning processes, analyzing time-series process data); (3) discussing opportunities for future research using Artificial Intelligence (AI) and external regulating agents (e.g., meta-learning, human digital twin), and (4) discussing implications for using multimodal learning analytics (MLA) for researchers, learners, and educators to induce and foster self-regulated learning.
About the speaker
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 Initiative. He received his PhD in Educational Psychology from McGill University and completed his postdoctoral training in Cognitive Psychology at Carnegie Mellon University. His main research area includes 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, 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), Department of Education, Institute of Education Sciences (IES), National Institutes of Health (NIH), and the Social Sciences and the Humanities Research Council of Canada (SSHRC), Natural and Sciences and Engineering Council of Canada (NSERC), Canada Research Chairs (CRC), Canadian Foundation for Innovation (CFI), European Association for Research on Learning and Instruction (EARLI) 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 National Science Foundation.