Automatic gaze-based detection of mind wandering during narrative film comprehension

Caitlin Mills, Robert Bixler, Xinyi Wang, Sidney K. D'Mello

Research output: Contribution to conferencePaperpeer-review

28 Scopus citations

Abstract

Mind wandering (MW) reflects a shift in attention from task-related to task-unrelated thoughts. It is negatively related to performance across a range of tasks, suggesting the importance of detecting and responding to MW in real-time. Currently, there is a paucity of research on MW detection in contexts other than reading. We addressed this gap by using eye gaze to automatically detect MW during narrative film comprehension, an activity that is used across a range of learning environments. In the current study, students self-reported MW as they watched a 32.5-minute commercial film. Students’ eye gaze was recorded with an eye tracker. Supervised machine learning models were used to detect MW using global (content-independent), local (content-dependent), and combined global+local features. We achieved a student-independent score (MW F1) of .45, which reflected a 29% improvement over a chance baseline. Models built using local features were more accurate than the global and combined models. An analysis of diagnostic features revealed that MW primarily manifested as a breakdown in attentional synchrony between eye gaze and visually salient areas of the screen. We consider limitations, applications, and refinements of the MW detector.

Original languageEnglish (US)
Pages30-37
Number of pages8
StatePublished - 2016
Externally publishedYes
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States
Duration: Jun 29 2016Jul 2 2016

Conference

Conference9th International Conference on Educational Data Mining, EDM 2016
Country/TerritoryUnited States
CityRaleigh
Period6/29/167/2/16

Bibliographical note

Funding Information:
This research was supported by the National Science Foundation (NSF) (DRL 1235958 and IIS 1523091). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.

Publisher Copyright:
© 2016 International Educational Data Mining Society. All rights reserved.

Keywords

  • Eye gaze
  • Film comprehension
  • Machine learning
  • Mind wandering

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