Detecting student engagement: Human versus machine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Engagement is complex and multifaceted, but crucial to learning. Computerized learning environments can provide a superior learning experience for students by automatically detecting student engagement (and, thus also disengagement) and adapting to it. This paper describes results from several previous studies that utilized facial features to automatically detect student engagement, and proposes new methods to expand and improve results. Videos of students will be annotated by third-party observers as mind wandering (disengaged) or not mind wandering (engaged). Automatic detectors will also be trained to classify the same videos based on students' facial features, and compared to the machine predictions. These detectors will then be improved by engineering features to capture facial expressions noted by observers and more heavily weighting training instances that were exceptionally-well classified by observers. Finally, implications of previous results and proposed work are discussed. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages317-320
Number of pages4
ISBN (Electronic)9781450343701
DOIs
StatePublished - Jul 13 2016
Externally publishedYes
Event24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada
Duration: Jul 13 2016Jul 17 2016

Publication series

NameUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Conference

Conference24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
CountryCanada
CityHalifax
Period7/13/167/17/16

Keywords

  • Affective computing
  • Engagement detection
  • Facial expressions

ASJC Scopus subject areas

  • Software

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