Methodological Issues in Multimodal Data to Measure Self-Regulatory Learning Processes with Intelligent Tutoring Systems

• In Event: Self- & Shared Regulation Learning

Short Description

We will present and discuss the methodological, analytical, and pedagogical challenges associated with measuring, understanding, and inferring real-time self-regulated learning processes by using examples from multichannel, multimodal data (e.g., log files, eye tracking, facial expressions of emotion, physiological data, and screen recordings of learner-system interactions) collected from several studies with undergraduates as they learned with MetaTutor, a hypermedia-based intelligent tutoring system.

Authors

• Presenter: Roger Azevedo, Department of Learning Sciences and Educational Research

• Presenter: Elizabeth B Cloude, Department of Learning Sciences and Educational Research, University of Central Florida

• Presenter: Daryn Dever, University of Central Florida

• Contributor: Wiedbusch Megan, University of Central Florida