Roger Azevedo
North Carolina State University
Quand?
24 octobre 2014
15h00
Où?
Local SU-1550
Pavillon Adrien Pinard de l'UQAM
100, rue Sherbrooke Ouest, Montréal
An interdisciplinary data fusion approach to measuring and fostering metacognition with advanced learning technologies
Learning involves the real-time deployment of cognitive, affective, metacognitive, and motivational (CAMM) processes. Traditional methods of measuring self-regulatory processes severely limit our understanding of the temporal nature and role of these processes during learning, problem solving, etc. Researchers from different disciplines have recently used advanced learning technologies (e.g., hypermedia, multi-agent systems, intelligent tutoring systems) to measure (detect, track, model) and foster self-regulatory processes during learning and problem solving. Despite the emergence of interdisciplinary research, much work is still needed given the various theoretical models and assumptions, methodological approaches (e.g., eye-tracking), data types (e.g., physiological data), analytical methods, etc. As such, my overall goal is to present an interdisciplinary data fusion approach to measuring and fostering metacognition with advanced learning technologies. More specifically, I will focus on: (1) presenting major conceptual, theoretical, and methodological issues for a data fusion approach that focuses on the real-time detection, tracking, and modeling of CAMM processes; (2) presenting recent data using interdisciplinary approaches that uses a multitude of techniques to detect, track, model CAMM processes while learning with advanced learning technologies; and, (3) outlining an interdisciplinary research agenda that has the potential to significantly enhance advanced learning technologies’ ability to provide real-time, individualized adaptive support of learners’ CAMM processes.