research-article

Improving Learning Outcomes with Gaze Tracking and Automatic Question Generation

Authors:

Kevyn Collins-Thompson,

Paul N. Bennett,

Shane Williams,

Dr. Wendy W. Tay,

Shamsi IqbalAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 1693 - 1703

https://doi.org/10.1145/3366423.3380240

Published: 20 April 2020 Publication History

Abstract

As AI technology advances, it offers promising opportunities to improve educational outcomes when integrated with an overall learning experience. We investigate forward-looking interactive reading experiences that leverage both automatic question generation and analysis of attention signals, such as gaze tracking, to improve short- and long-term learning outcomes. We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior. We also compare the effectiveness of manually-written questions with those produced by Automatic Question Generation (AQG). We further investigate gaze and reading patterns indicative of low vs. high learning in both short- and long-term scenarios (one-week followup). We show AQG-generated adjunct questions have promise as a way to scale to a wide variety of reading material where the cost of manually curating questions may be prohibitive.

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Cited By

Urgo KArguello J(2024)The Effects of Goal-setting on Learning Outcomes and Self-Regulated Learning ProcessesProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638348(278-290)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638348
Zhu PCâmara ARoy NMaxwell DHauff C(2024)On the Effects of Automatically Generated Adjunct Questions for Search as LearningProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638332(266-277)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638332
Bisogni CNappi MTortora GDel Bimbo A(2024)Gaze analysisImage and Vision Computing10.1016/j.imavis.2024.104961144:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.104961
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Index Terms

Improving Learning Outcomes with Gaze Tracking and Automatic Question Generation
1. Applied computing
  1. Education
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Urgo KArguello J(2024)The Effects of Goal-setting on Learning Outcomes and Self-Regulated Learning ProcessesProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638348(278-290)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638348
Zhu PCâmara ARoy NMaxwell DHauff C(2024)On the Effects of Automatically Generated Adjunct Questions for Search as LearningProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638332(266-277)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638332
Bisogni CNappi MTortora GDel Bimbo A(2024)Gaze analysisImage and Vision Computing10.1016/j.imavis.2024.104961144:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.104961
Li PDong SZhang YZhang B(2023)Predicting Task Planning Ability for Learners Engaged in Searching as Learning Based on Tree-Structured Long Short-Term Memory NetworksApplied Sciences10.3390/app13231284013:23(12840)Online publication date: 30-Nov-2023
https://doi.org/10.3390/app132312840
Mysore SJasim MSong HAkbar SRandall AMahyar N(2023)How Data Scientists Review the Scholarly LiteratureProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578309(137-152)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578309
Medeiros Machado GBonnin GCastagnos SHoareau LThomas ATazouti Y(2023)Modelling children's inhibitory skills using learning data from an educational appJournal of Computer Assisted Learning10.1111/jcal.1277339:3(856-868)Online publication date: 15-Feb-2023
https://doi.org/10.1111/jcal.12773
Al Faraby SAdiwijaya ARomadhony A(2023)Review on Neural Question Generation for Education PurposesInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00374-xOnline publication date: 31-Oct-2023
https://doi.org/10.1007/s40593-023-00374-x
Sanfilippo FBlazauskas TSalvietti GRamos IVert SRadianti JMajchrzak TOliveira D(2022)A Perspective Review on Integrating VR/AR with Haptics into STEM Education for Multi-Sensory LearningRobotics10.3390/robotics1102004111:2(41)Online publication date: 31-Mar-2022
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Steuer TFilighera ATregel TMiede A(2022)Educational Automatic Question Generation Improves Reading Comprehension in Non-native Speakers: A Learner-Centric Case StudyFrontiers in Artificial Intelligence10.3389/frai.2022.9003045Online publication date: 10-Jun-2022
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Smith CUrgo KArguello JCapra RElsweiler DKruschwitz ULudwig B(2022)Learner, Assignment, and Domain: Contextualizing Search for ComprehensionProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505819(191-201)Online publication date: 14-Mar-2022
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