Italian Journal of Health Education, Sports and Inclusive Didactics
COMPARING UDL AND TRADITIONAL E-LEARNING COURSES: LEVERAGING EDUCATIONAL DATA MINING TO ENHANCE SELF-REGULATED LEARNING AND MOTIVATION
Abstract
This work is presented as a position paper aimed at introducing an analytical framework accompanied by a project proposal comparing two university courses: one implemented according to the principles of Universal Design for Learning (UDL) and the other using a traditional approach, utilizing Educational Data Mining (EDM) to analyze data indicative of learning processes. The objective is to assess how the UDL approach influences Self-Regulated Learning (SRL) processes and student motivation. The project aims to structure a computational theoretical framework encompassing variables such as engagement, perceived self-efficacy, and academic outcomes, to identify the advantages of UDL implementation in enhancing self-regulation and learning motivation. This approach allows for evaluating the effectiveness of the two educational strategies, providing concrete data to optimize the learning experience and improve academic results.
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DOI: https://doi.org/10.32043/gsd.v9i2.1506
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Italian Journal of Health Education, Sports and Inclusive Didactics
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