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35.031 - Multimodal Data During Learning With Advanced Learning Technologies: What Does Evidence Reveal About Self-Regulated Learning?
Sat, April 6, 4:10 to 6:10pm, Sheraton Centre Toronto Hotel, Floor: Mezzanine Level, Willow CentreSession Type: Symposium
Abstract
Multimodal trace data (log files, facial expressions of emotions, physiological sensors, concurrent think-alouds, discourse between human and artificial agents) collected during students’ real-time interactions with advanced learning technologies (ALTs) such as intelligent tutoring systems, serious games, open-ended learning environments, is transforming our understanding of the complex nature of self-regulated learning (SRL). The goal of this symposium is to present empirical evidence of using multimodal trace data to examine cognitive, affective, metacognitive, and motivational SRL processes during learning with different types of ALTs, and discuss the challenges faced when conducting this research. Our symposium will contribute to an emerging area of research on SRL that takes an interdisciplinary approach to address the complex nature of SRL using multimodal trace data.
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Papers
Multimodal Data Analysis of Students' Self-Regulated Learning Behaviors in Open-Ended Learning Environments - Gautam Biswas, Vanderbilt University; Ramkumar Rajendran, Vanderbilt University; Anabil Munshi, Vanderbilt University
The Functional Roles of Cognition and Emotion in Predicting Clinical Reasoning Performance - Susanne P. Lajoie, McGill University; Shan Li, McGill University; Juan Zheng, McGill University
Do Eye Movements Contribute to Accurate Metacognitive Judgments During Multimedia Learning? - James Lester, North Carolina State University; Nicholas Vincent Mudrick, North Carolina State University; Roger Azevedo, University of Central Florida
How Do Contextual Emotions Differ for Self-Regulated Learning and Scientific Reasoning During Game-Based Learning? - Michelle Taub, University of Central Florida; Robert Sawyer, North Carolina State University; James Lester, North Carolina State University; Roger Azevedo, University of Central Florida
Understanding Self-Regulatory Processes Using Multimodal Trace Data During Human-Machine Interactions With an Intelligent Tutoring System - Roger Azevedo, University of Central Florida; Michelle Taub, University of Central Florida; Elizabeth Brooke Cloude, University of Central Florida; Megan Price, University of Central Florida