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International Conference on User Modeling, Adaptation, and Personalization

UMAP 2014: User Modeling, Adaptation, and Personalization pp 37–48Cite as

Toward Fully Automated Person-Independent Detection of Mind Wandering

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8538))

Abstract

Mind wandering is a ubiquitous phenomenon where attention involuntary shifts from task-related processing to task-unrelated thoughts. Mind wandering has negative effects on performance, hence, intelligent interfaces that detect mind wandering can intervene to restore attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudo-randomly probed to report mind wandering instances while an eye tracker recorded their gaze during a computerized reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from gaze and context features in a user-independent fashion. Mind wandering was predicted with an accuracy of 72% (expected accuracy by chance was 62%) when probed at the end of a page and an accuracy of 59% (chance was 50%) when probed in the midst of reading a page. Possible improvements to the detectors and applications are discussed.

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Author information

Authors and Affiliations

  1. Departments of Computer Science, University of Notre Dame, Notre Dame, IN, 46556, US

    Robert Bixler & Sidney D’Mello

  2. Departments of Psychology, University of Notre Dame, Notre Dame, IN, 46556, US

    Sidney D’Mello

Authors
  1. Robert Bixler
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  2. Sidney D’Mello
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Editor information

Editors and Affiliations

  1. School of Computing, University of Leeds, United Kingdom

    Vania Dimitrova

  2. University of Haifa, Israel

    Tsvi Kuflik

  3. Department of Information and Computer Sciences, University of Hawaii, 96822, Honolulu, HI, USA

    David Chin

  4. Free University of Bozen - Bolzano, Piazza Domenicani 3, 39100, Bolzano, Italy

    Francesco Ricci

  5. Department of Computer Science, Aalborg University, Selma Lagerlofs Vej, 300 9220, Aalborg-East, Denmark

    Peter Dolog

  6. EEMCS - Web Information Systems, 2600 GA, Delft, The Netherlands

    Geert-Jan Houben

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© 2014 Springer International Publishing Switzerland

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Bixler, R., D’Mello, S. (2014). Toward Fully Automated Person-Independent Detection of Mind Wandering. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-08786-3_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08785-6

  • Online ISBN: 978-3-319-08786-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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