GazeMotive: A Gaze-Based Motivation-Aware E-Learning Tool for Students with Learning Difficulties

Ruijie Wang, Yuanchen Xu, Liming Chen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

We developed a gaze-based motivation-aware e-learning tool, a Windows desktop learning application, for students with learning difficulties that aims at motivation-enhanced learning by dynamically assessing and responding to students’ motivational states based on the motivation model that we developed previously using rigorous methodologies including domain knowledge and empirical studies with participants with learning difficulties. The learning application uses an eye tracker to monitor a user’s eye movements during the user’s learning process, assesses the user’s motivational states using the prediction models we developed before to output personalised feedback from a pedagogical agent in the system based on both the eye gaze features and user’s self-input data for enhancing users’ motivation and engagement in real-time. Our e-learning tool is an example of applying user modelling and personalisation to an e-learning environment targeting at users’ learning motivation, producing great insight on how eye tracking can assist with students’ learning motivation and engagement in e-learning environments.
Original languageEnglish
Title of host publicationINTERACT 2019, Lecture Notes in Computer Science
Place of PublicationPaphos, Cyprus
PublisherSpringer Cham
Pages544-548
Number of pages4
Volume11749
ISBN (Electronic)978-3-030-29390-1
ISBN (Print)978-3-030-29389-5
DOIs
Publication statusPublished - 23 Aug 2019

Publication series

NameHuman-Computer Interaction – INTERACT 2019
Volume11749
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • EYE TRACKING
  • MOTIVATION ASSESSMENT
  • PERSONALISED FEEDBACK

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