Using multimodal process data to measure self-regulatory processes during learning with advanced learning technologies: Opportunities and challenges

  • october 14th 2019

  • 1PM — 3PM

  • amphitheater I — Faculty of Psychology ULisboa

Roger Azevedo
University of Central Florida
Departments of Learning Sciences and Educational research and Computer Science

Learning involves the real-time deployment of cognitive, affective, metacognitive, and motivational (CAMM) 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, etc. Interdisciplinary researchers have recently used advanced learning technologies (e.g., intelligent tutoring systems, serious games, simulations, virtual reality) to measure (i.e., 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., log-files, eye-tracking), data types (e.g., verbal data, physiological data), analytical methods, etc. In this presentation, I will present an interdisciplinary data fusion approach to measuring and fostering self-regulated learning with advanced learning technologies. More specifically, I will focus on: (1) presenting major theoretical and methodological challenges for a data fusion approach that focus on the real-time detection, tracking, and modeling of CAMM processes; (2) presenting recent multimodal multichannel data used to detect, track, and 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, intelligent support of learners’ CAMM processes.

Organized under the Interuniversity PhD Program in Educational Psychology