Date of Award:
12-2023
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Hamid Karimi
Committee
Hamid Karimi
Committee
John Edwards
Committee
Shuhan Yuan
Abstract
Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we identified important features from student activity data to improve grade prediction, helping identify at-risk students early and monitor their progress. Lastly, we used graph representation learning to understand complex relationships in the data, leading to more accurate predictive models. This research enhances our understanding of data mining in online learning, with implications for personalized learning and support mechanisms.
Checksum
7862d1c1cdf458fcdea857f132fa77b5
Recommended Citation
Yaramala, Aswani, "Analysis of Student Behavior and Score Prediction in Assistments Online Learning" (2023). All Graduate Theses and Dissertations, Fall 2023 to Present. 84.
https://digitalcommons.usu.edu/etd2023/84
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