Abstract Book of the 9th International Conference on Research in Education
Year: 2025
DOI:
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Enhancing Accessibility Education: Using Machine Learning to Explore Patterns in Student Confidence Levels Towards Accessibility in Technology
Dr. Anastasia Angelopoulou
ABSTRACT:
Machine learning is increasingly recognized as a transformative tool in education, with the potential to enhance predictive analytics and provide more accurate results. Despite its growing application, the impact of machine learning in educational contexts remains limited, necessitating further research to develop generalizable results. Accessibility education studies, in particular, do not usually involve machine learning techniques to analyze data. This study aims to contribute to the field by exploring the application of machine learning techniques in the context of education and, particularly, of accessibility education. In this study, we introduced students to the principles of designing, developing, and evaluating accessible applications in computer science courses
zacross three academic semesters. We then utilized factor analysis, cluster analysis, and correlation analysis to evaluate the impact of integrating accessibility concepts into the curriculum. Our
research surveyed 74 undergraduate computer science students. The introduction of accessibility concepts led to significant improvements in students’ confidence levels and established stronger correlations among confidence, interest, and familiarity with accessibility concepts. Our findings emphasize the significance of introducing accessibility concepts early into the curriculum. This study offers insights for faculty seeking to integrate or develop accessibility-related activities in their courses as well as using machine learning to analyze data in accessibility educational contexts.
keywords: Accessibility, Cluster Analysis, Correlation Analysis, Education, Factor Analysis