Detecting student emotions in computer-enabled classrooms

Nigel Bosch, Sidney K. D'Mello, Ryan S. Baker, Jaclyn Ocumpaugh, Valerie Shute, Matthew Ventura, Lubin Wang, Weinan Zhao

Research output: Contribution to journalConference articlepeer-review

Abstract

Affect detection is a key component of intelligent educational interfaces that can respond to the affective states of students. We use computer vision, learning analytics, and machine learning to detect students' affect in the real-world environment of a school computer lab that contained as many as thirty students at a time. Students moved around, gestured, and talked to each other, making the task quite difficult. Despite these challenges, we were moderately successful at detecting boredom, confusion, delight, frustration, and engaged concentration in a manner that generalized across students, time, and demographics. Our model was applicable 98% of the time despite operating on noisy realworld data.

Original languageEnglish (US)
Pages (from-to)4125-4129
Number of pages5
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2016-January
StatePublished - Jan 1 2016
Externally publishedYes
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: Jul 9 2016Jul 15 2016

ASJC Scopus subject areas

  • Artificial Intelligence

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