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Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms by Nigel Bosch; Sidney K. D'mello; Jaclyn Ocumpaugh; Ryan S. Baker; Valerie Shute is a Computer Science article available to read on EtoBox.

What is Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms about?

Affect detection is a key component in intelligent educational interfaces that respond to students’ affective states. We use computer vision and machine-learning techniques to detect students’ affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. We collected data in the real-world environment of a school computer lab with up to 30 students simultaneously playing the game while moving around, gesturing, and talking to each other. The results were cross-validated at the student level to ensure generalization to new students. Classification accuracies, quantified as area under the receiver operating characteristic curve (AUC), were above chance (AUC of 0.5) for all the affective states observed, namely, boredom (AUC = .610), confusion (AUC = .649), delight (AUC = .867), engagement (AUC = .679), frustration (AUC = .631), and for off-task behavior (AUC = .816). Furthermore, the detectors showed temporal generalizability in that there was less than a 2% decrease in accuracy when tested on data collected from different times of the day and from different days. There was also some evidence of gen

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Author

Nigel Bosch; Sidney K. D'mello; Jaclyn Ocumpaugh; Ryan S. Baker; Valerie Shute

Publisher

ACM

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)