| 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 |