Dr. Phil Winne presents “Learning Analytics for Self-Regulated Learning: Frameworks, Methods & Future Work” (moderated by Dr. Jason Chen and Dr. David Morris). Webinar hosted by APA Division 15 on September 30, 2020.
Download Dr. Phil Winne’s slides.
Summary: This is a scholarly presentation that introduces three models of self-regulated learning to guide the collection and interpretation of learner data. Specifically, the presentation emphasizes the importance of collecting and analyzing learning process data such as real-time trace and ambient data (that is, using learning analytics) . Learning analytics would be helpful to understand the change in the learning behaviors and interpret this change more meaningfully.
Dr. Winne posits that the design of traditional experimental research (i.e., randomized controlled trial) is inadequate to inform self-regulated learning and puts forth a merging of models and introduces an open source software called “SPInStudy.” This software is intended to better reflect or measure self-regulated learning in real-time, using trace and ambient data.