Professor in the Department of Psychology in the area of Human Factors and Applied Cognition at North Carolina State University
Roger Azevedo is a Professor in the Department of Psychology in the area of Human Factors and Applied Cognition at North Carolina State 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). 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. 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 200 peer-reviewed papers, chapters, and refereed conference proceedings in the areas of educational, learning, cognitive, educational, and computational sciences. He is the editor of the Metacognition and Learning journal and also serves on the editorial board of several top-tiered learning and cognitive sciences journals (e.g., Educational Psychologist, International Journal of AI in Education). His research is funded by the National science Foundation, Institute of Education Sciences, and the Social Sciences and the Humanities Research Council of Canada. He is a fellow of the American Psychological Association and the recipient of the prestigious Early Faculty Career Award from the National Science Foundation.
Fostering metacognition using multimodal multichannel process data: Challenges for Learning Analytics
A major, and often overlooked, issue in game development is efficiently teaching users how to play games. Difficulty with understanding strategies and mechanics often leads to player frustration, and frustrated players quit. Learning is complex, and involves the interplay of cognitive, affective, metacognitive, and motivational processes. Traditional methods of measuring learning (such as self reporting and surveys) do a poor job of helping us understand what is and isn’t working for our users. This talk will outline the challenges of tracking and modeling learning processes in players, present a framework for making use of this data, and explain future implications of this work for building intelligent teaching systems to help players better understand games.