Predictive Analysis of Academic Achievement in Information Studies: A Comparative Study Using Educational Data Mining Techniques
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Abstract
Predicting students’ academic achievement in the initial stages is beneficial for designing effective training programs to enhance success rates. Extracting knowledge from student data is a fundamental aspect of Educational Data Mining (EDM). This study aims to analyze the predictive factors influencing the outcomes of graduates from the Information Studies program. The results not only contribute to improving student performance but also aid in constructing a better curriculum. A dataset is utilized within five classification models to categorize students into four target classes. The datasets are grouped into three types: Demographic information, course grades, and early-stage GPA. This study addresses the issue of the imbalanced dataset by applying the Synthetic Minority Over-sampling Technique (SMOTE). The findings indicate that early-stage GPA (90.5%) is the most significant predictor, particularly when applying the Naive Bayes classifier on a balanced dataset. In contrast, demographic information (58.0%) and core course grades (87.5%) show lower predictive influence. The findings support learning strategies and enhancing curriculum design to improve final academic outcomes.
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