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What Can Multimodal Data Streams Reveal About Students’ Self-Regulated Learning?

Sat, April 14, 12:25 to 1:55pm, Crown Plaza Times Square, Floor: Fourth Floor, Times Square B Room

Session Type: Symposium

Abstract

Multimodal trace data (from log files, facial expressions of emotions, physiological sensors, discourse between multiple human and artificial agents) collected during students’ real-time interactions with advanced learning learning (ALTs) such as intelligent tutoring systems, serious games, and collaborative systems, is transforming our understanding of self-regulated learning (SRL). This symposium includes a group of researchers who will present and discuss different multimodal trace data and answer the following questions: (1) what do multimodal trace data reveal about the nature of the underlying cognitive, affective, metacognitive, motivational, and social processes SRL processes across tasks, domains, contexts, and ALTs? and (2) what are the challenges posed by (1) for current methodological and analytical approaches for studying SRL.

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