The eyes have it: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System

Stephen Hutt, Caitlin Mills, Shelby White, Patrick J. Donnelly, Sidney K. D’Mello

Research output: Contribution to conferencePaperpeer-review

56 Scopus citations

Abstract

Mind wandering (MW) is a ubiquitous phenomenon characterized by an unintentional shift in attention from task-related to task-unrelated thoughts. MW is frequent during learning and negatively correlates with learning outcomes. Therefore, the next generation of intelligent learning technologies should benefit from mechanisms that detect and combat MW. As an initial step in this direction, we used eye-gaze and contextual information (e.g., time into session) to build an automated MW detector as students interact with GuruTutor – an intelligent tutoring system (ITS) for biology. Students self-reported MW by responding to pseudorandom thought-probes during the tutoring session while a consumer-grade eye tracker monitored their eye movements. We used supervised machine learning techniques to discriminate between positive and negative responses to the probes in a student-independent fashion. Our best results for detecting MW (F1 of 0.49) were obtained with an evolutionary approach to develop topologies for neural network classifiers. These outperformed standard classifiers (F1 of 0.43 with a Bayes net) and a chance baseline (F1 of 0.19). We discuss our results in the context of integrating MW detection into an attention-aware version of GuruTutor.

Original languageEnglish (US)
Pages86-93
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

  • Attention-aware learning
  • Eye-gaze
  • Intelligent tutoring systems
  • Mind wandering

Fingerprint

Dive into the research topics of 'The eyes have it: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System'. Together they form a unique fingerprint.

Cite this