Work-In-Progress: Investigate Eye-Tracking Metrics and Effectiveness of Visual Learning Aids in Online Learning Environments for Students With Learning Disabilities Using Machine Learning
DOI:
https://doi.org/10.33423/jhetp.v24i9.7220Keywords:
higher education, philosophy of engineering education, K-12, STEM, factor analysis, data correlationAbstract
This Work-In-Progress study proposes a novel approach to explore the learning behaviors of students with learning disabilities in online learning environments by investigating eye-tracking metrics and effectiveness of visual learning aids. It builds on previous research that suggests that students with learning disabilities often face difficulties in online learning environments due to the lack of visual cues and their limited ability to interact with the learning material. The use of machine learning to analyze eye-tracking data and visual learning aids can provide insights into how students with learning disability interact with online learning materials, with the goal of improving the learning outcomes as well as informing the design of constructive teaching strategies and seeking to expand this research to the online learning context
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