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A study on eye fixation patterns of students in higher education using an online learning system

Published: 25 April 2016 Publication History

Abstract

We study how the use of online learning systems stimulate cognitive activities, by conducting an experiment with the use of eye tracking technology to monitor eye fixations of 60 final year students engaging in online interactive tutorials at the start of their Final Year Project module. Our findings show that the students' visual scanning behaviours fall into three different types of eye fixation patterns, and the data corresponding to the different types relates to the performance of the students in other related academic modules. We conclude that this method of studying eye fixation patterns can identify different types of learners with respect to cognitive activities and academic potentials, allowing educators to understand how their instructional design using online learning environments can stimulate higher-order cognitive activities.

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  • (2023)What to Keep, What to DiscardHandbook of Research on Revisioning and Reconstructing Higher Education After Global Crises10.4018/978-1-6684-5934-8.ch015(305-318)Online publication date: 20-Jan-2023
  • (2021)Neurophysiological Measurements in Higher Education: A Systematic Literature ReviewInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00256-032:2(413-453)Online publication date: 16-Jun-2021
  • (2020)Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking DataSensors10.3390/s2007194920:7(1949)Online publication date: 31-Mar-2020
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cover image ACM Other conferences
LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
April 2016
567 pages
ISBN:9781450341905
DOI:10.1145/2883851
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 April 2016

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Author Tags

  1. cognitive activity
  2. eye tracking
  3. human-computer interaction
  4. instructional design
  5. online learning

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LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2023)What to Keep, What to DiscardHandbook of Research on Revisioning and Reconstructing Higher Education After Global Crises10.4018/978-1-6684-5934-8.ch015(305-318)Online publication date: 20-Jan-2023
  • (2021)Neurophysiological Measurements in Higher Education: A Systematic Literature ReviewInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00256-032:2(413-453)Online publication date: 16-Jun-2021
  • (2020)Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking DataSensors10.3390/s2007194920:7(1949)Online publication date: 31-Mar-2020
  • (2020)Visualization and Analysis for Supporting Teachers Using Clickstream Data and Eye Movement DataDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-50344-4_42(581-592)Online publication date: 10-Jul-2020
  • (2018)Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomyWIREs Data Mining and Knowledge Discovery10.1002/widm.12438:3Online publication date: 30-Jan-2018

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