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Exploring the Viability of Using Eye Tracking to Detect Neurodivergent Learners' Implicit Learning in a Physics Game

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Title: Exploring the Viability of Using Eye Tracking to Detect Neurodivergent Learners' Implicit Learning in a Physics Game
Language: English
Authors: Ibrahim Dahlstrom-Hakki, Elizabeth Rowe, Jodi Asbell-Clarke, Mia Almeda
Source: Computer-Based Learning in Context. 2024 6(1):24-40.
Availability: University of Pennsylvania. 3451 Walnut Street, Philadelphia, PA 19104. e-mail: cb.learningincontext@gmail.com; Web site: https://learninganalytics.upenn.edu/CBLC/
Peer Reviewed: Y
Page Count: 17
Publication Date: 2024
Sponsoring Agency: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: 1417456
1417967
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, Eye Movements, Physics, Science Instruction, Game Based Learning, Neurodevelopmental Disorders, Student Evaluation, Academic Achievement, Learning Processes, Behavior Development, Computer Assisted Instruction
ISSN: 2690-1307
Abstract: With the prominence of assessments in education, there is an increasing need to create new forms of assessment that more accurately reflect the needs of the entire student population, particularly neurodivergent learners. To address this challenge, this paper explores the potential for using eye tracking data in a game-based learning environment to assess student's implicit knowledge. Data was collected from a sample of 66 neurodivergent college students playing the physics game Impulse while their eye movements and game play behaviors were recorded. The results indicate that gaze allocation patterns were predictive of students' physics knowledge and aligned with previously identified behavior indicators of learning. These findings provide evidence for further development of eye movement-based assessments in computer-based instruction and demonstrate how these data can be collected, organized, and analyzed.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1437265
Database: ERIC
FullText Text:
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  Data: Exploring the Viability of Using Eye Tracking to Detect Neurodivergent Learners' Implicit Learning in a Physics Game
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  Data: <searchLink fieldCode="SO" term="%22Computer-Based+Learning+in+Context%22"><i>Computer-Based Learning in Context</i></searchLink>. 2024 6(1):24-40.
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  Data: University of Pennsylvania. 3451 Walnut Street, Philadelphia, PA 19104. e-mail: cb.learningincontext@gmail.com; Web site: https://learninganalytics.upenn.edu/CBLC/
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  Data: With the prominence of assessments in education, there is an increasing need to create new forms of assessment that more accurately reflect the needs of the entire student population, particularly neurodivergent learners. To address this challenge, this paper explores the potential for using eye tracking data in a game-based learning environment to assess student's implicit knowledge. Data was collected from a sample of 66 neurodivergent college students playing the physics game Impulse while their eye movements and game play behaviors were recorded. The results indicate that gaze allocation patterns were predictive of students' physics knowledge and aligned with previously identified behavior indicators of learning. These findings provide evidence for further development of eye movement-based assessments in computer-based instruction and demonstrate how these data can be collected, organized, and analyzed.
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    Languages:
      – Text: English
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        PageCount: 17
        StartPage: 24
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      – SubjectFull: College Students
        Type: general
      – SubjectFull: Eye Movements
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      – SubjectFull: Physics
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      – SubjectFull: Science Instruction
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      – SubjectFull: Neurodevelopmental Disorders
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      – SubjectFull: Learning Processes
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      – SubjectFull: Behavior Development
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      – SubjectFull: Computer Assisted Instruction
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      – TitleFull: Exploring the Viability of Using Eye Tracking to Detect Neurodivergent Learners' Implicit Learning in a Physics Game
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