Temporal generalizability of face-based affect detection in noisy classroom environments

Nigel Bosch, Sidney D’Mello, Ryan Baker, Jaclyn Ocumpaugh, Valerie Shute

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

Abstract

The goal of this paper was to explore the possibility of generalizing face-based affect detectors across multiple days, a problem which plagues physiological- based affect detection. Videos of students playing an educational physics game were collected in a noisy computer-enabled classroom environment where students conversed with each other, moved around, and gestured. Trained observers provided real-time annotations of learning-centered affective states (e.g., boredom, confusion) as well as off-task behavior. Detectors were trained using data from one day and tested on data from different students on another day. These cross-day detectors demonstrated above chance classification accuracy with average Area Under the ROC Curve (AUC,.500 is chance level) of.658, which was similar to within-day (training and testing on data collected on the same day) AUC of.667. This work demonstrates the feasibility of generalizing face-based affect detectors across time in an ecologically valid computer-enabled classroom environment.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
PublisherSpringer-Verlag
Pages44-53
Number of pages10
ISBN (Print)9783319197722
DOIs
StatePublished - Jan 1 2015
Externally publishedYes
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: Jun 22 2015Jun 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Artificial Intelligence in Education, AIED 2015
CountrySpain
CityMadrid
Period6/22/156/26/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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