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

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 conference › Paper › peer-review

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 language	English (US)
Pages	86-93
Number of pages	8
State	Published - 2016
Externally published	Yes
Event	9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: Jun 29 2016 → Jul 2 2016

Conference

Conference	9th International Conference on Educational Data Mining, EDM 2016
Country/Territory	United States
City	Raleigh
Period	6/29/16 → 7/2/16

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

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

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.",

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author = "Stephen Hutt and Caitlin Mills and Shelby White and Donnelly, {Patrick J.} and D{\textquoteright}Mello, {Sidney K.}",

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: {\textcopyright} 2016 International Educational Data Mining Society. All rights reserved.; 9th International Conference on Educational Data Mining, EDM 2016 ; Conference date: 29-06-2016 Through 02-07-2016",

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language = "English (US)",

pages = "86--93",

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T2 - 9th International Conference on Educational Data Mining, EDM 2016

AU - Hutt, Stephen

AU - Mills, Caitlin

AU - White, Shelby

AU - Donnelly, Patrick J.

AU - D’Mello, Sidney K.

N1 - 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.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

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KW - Intelligent tutoring systems

KW - Mind wandering

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

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Keywords

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