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Session Type: Symposium
Multimodal analysis of teachers’ instructional practices and students’ collaborative learning offer new opportunities to provide feedback to teachers and help them improve their practices. Recent technological advances in machine perception (i.e., machine learning and its applications to computer vision, speech recognition, natural language processing, etc.) provide novel directions for these efforts. This symposium focuses on research designed to advance machine perception in recognizing activities in classroom audiovisual data and text data and making judgments about instruction and the learning environment. The included papers span elementary and secondary classrooms, different content areas, and multiple national contexts to consider ways that machine perception can make the process of analyzing audiovisual and text data more meaningful and timely.
Using A Novel Audio-Video Transformer for Classifying Elementary Instructional Activities - Peter A. Youngs, University of Virginia; Jonathan K. Foster, University at Albany - SUNY; Matthew Korban, University of Virginia; Scott T. Acton, University of Virginia
Validating Automated Teaching Effectiveness with Multimodal Data - Tim Fütterer, University of Tübingen; Ruikun Hou, University of Tubingen; Babette Buhler, Technical University of Munich; Efe Bozkir, Technical University of Munich; Courtney A. Bell, University of Wisconsin - Madison; Enkelejda Kasneci, University of Tübingen; Peter Gerjets, University of Tübingen; Ulrich Trautwein, University of Tübingen
Generalizable AI Models for Classifying Student Collaborative Discourse in Classrooms - Chelsea Chandler, University of Colorado - Boulder; Sidney K. D'Mello, University of Colorado - Boulder
Decoding Instructional Dialogue: Human-AI Collaborative Analysis of Teacher Use of AI Tool at Scale - Alex Liu, University of Washington; William Lief Evison Esbenshade, Google Inc; Shawon Sarkar, University of Washington; Zewei Tian, University of Washington; Zachary Zhang, University of Washington; Kevin He, University of Washington; Min Sun, University of Washington
AI-Driven Embedded Assessment Agent for Enhancing University Students' Online Collaborative Learning Engagement and Self-Efficacy - Fa Liu, East China Normal University; Dandan Gao, East China Normal University; Haoming Wang, East China Normal University