Using Multimodal Multichannel Process Data to Understand and Foster Self-Regulated Learning with Advanced Learning Technologies: Opportunities and Challenges

This seminar took place through Zoom on November 19th, 2020 from 1:00 pm to 2:00 pm.
Presented by: Roger Azevedo, Ph.D., Professor, Department of Learning Sciences and Educational Research, UCF


Click Here to Access Seminar Recording


Follow these steps to gain access to the recording:

  1. Click on the link above and login to Zoom with SSO using UCF as your company domain
  2. Enter your NID username and password





Using Multimodal Multichannel Process Data to Understand and Foster Self-Regulated Learning with Advanced Learning Technologies: Opportunities and Challenges

Learning involves real-time deployment of cognitive, affective, metacognitive, motivational, and social processes. Traditional methods of measuring self-regulatory processes (e.g., self-reports) severely limit our understanding of the temporal nature and role of these processes during learning, problem solving, reasoning, etc. Interdisciplinary researchers have recently used advanced learning technologies (e.g., intelligent tutoring systems, serious games, simulations, immersive virtual environments) to enhance learning by inducing, fostering, and supporting self-regulatory processes while using advanced learning technologies. Despite the emergence of interdisciplinary research, much work is still needed given the various theoretical models and assumptions underlying human learning, methodological approaches (e.g., log-files, eye-tracking, physiological sensors), data types (e.g., verbal, non-verbal, physiological), and analytical methods (e.g., statistics, data mining, machine learning). In this presentation, I will focus on several major challenges currently facing researchers, trainers, educators, and industry partners, including: (1) theoretical and methodological
challenges related to real-time detection, tracking, and modeling of self-regulatory processes; (2) recent work on using multimodal multichannel data to detect, track, and model self-regulatory processes while learning with advanced learning technologies; and, (3) outlining an interdisciplinary research agenda and opportunities that have the potential to significantly enhance advanced learning technologies’ ability to provide real-time, intelligent support of learning, problem solving, and reasoning across domains.

Dr. Roger Azevedo is a Professor in the Department of Learning Sciences and Educational Research 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 has published over 300 peer-reviewed papers, chapters, and refereed conference proceedings. 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 (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), and Canadian Foundation for Innovation (CFI). 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, and motivational processes and their impact on learning, performance, and transfer.