User Modeling and User-Adapted Interaction

, Volume 26, Issue 1, pp 33–68 | Cite as

Automatic gaze-based user-independent detection of mind wandering during computerized reading

  • Robert BixlerEmail author
  • Sidney D’Mello


Mind wandering is a ubiquitous phenomenon where attention involuntarily shifts from task-related thoughts to internal task-unrelated thoughts. Mind wandering can have negative effects on performance; hence, intelligent interfaces that detect mind wandering can improve performance by intervening and restoring 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 pseudorandomly probed to report mind wandering while an eye tracker recorded their gaze during the reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from eye gaze and context features in a user-independent fashion. Mind wandering was detected with an accuracy of 72 % (expected accuracy by chance was 60 %) when probed at the end of a page and an accuracy of 67 % (chance was 59 %) when probed in the midst of reading a page. Global gaze features (gaze patterns independent of content, such as fixation durations) were more effective than content-specific local gaze features. An analysis of the features revealed diagnostic patterns of eye gaze behavior during mind wandering: (1) certain types of fixations were longer; (2) reading times were longer than expected; (3) more words were skipped; and (4) there was a larger variability in pupil diameter. Finally, the automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity. Possible improvements to the detector and applications that utilize the detector are discussed.


Gaze tracking Mind wandering User modeling 



We would first like to thank our collaborators at the University of Memphis for assistance with data collection. We also thank Kris Kopp, Caitlin Mills, Nigel Bosch, Jennifer Neale, Jacqueline Kory, Jonathan Cobian, and Matthew Hunter for help with data collection and analysis. The authors would also like to thank the individuals who reviewed the initial draft of this paper prior to publication. This research was supported by the National Science Foundation (NSF) (ITR 0325428, HCC 0834847, DRL 1235958).


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© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Notre DameNotre DameUSA

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