Viewing Student Affect and Learning through Classroom Observation and Physical Sensors

Authors:
Toby Dragon;Ivon Arroyo;Beverly P. Woolf;Winslow Burleson;Rana Kaliouby;Hoda Eydgahi
Affiliations:
Department of Computer Science, University of Massachusetts, Amherst,;Department of Computer Science, University of Massachusetts, Amherst,;Department of Computer Science, University of Massachusetts, Amherst,;Arts, Media and Engineering Program, Arizona State University,;Media Lab, Massachusetts Institute of Technology,;Department of Electrical Engineering, Massachusetts Institute of Technology,
Venue:
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Year:
2008

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Abstract

We describe technology to dynamically collect information about students' emotional state, including human observation and real-time multi-modal sensors. Our goal is to identify physical behaviors that are linked to emotional states, and then identify how these emotional states are linked to student learning. This involves quantitative field observations in the classroom in which researchers record the behavior of students who are using intelligent tutors. We study the specific elements of learner's behavior and expression that could be observed by sensors. The long-term goal is to dynamically predict student performance, detect a need for intervention, and determine which interventions are most successful for individual students and the learning context (problem and emotional state).