Uncovering Hidden Patterns in Student Academic Performance Using Association Rule Mining: An Apriori Approach

The increasing availability of educational data in higher institutions has created opportunities for data-driven approaches to improve academic performance and institutional decision-making. However, many existing predictive models, such as neural networks and ensemble methods, often lack interpretability, limiting their practical usefulness for academic stakeholders. This study addresses this challenge by applying the Apriori algorithm, an association rule mining technique, to uncover interpretable patterns in university students’ academic performance data.
Using a structured dataset comprising students’ course results, continuous assessment scores, and enrollment records, the study employs data preprocessing techniques, including discretization and transformation, to prepare the data for analysis. The Apriori algorithm is then implemented to generate frequent itemsets and association rules based on defined support, confidence, and lift thresholds. The resulting rules reveal meaningful relationships between academic variables, such as the influence of continuous assessment performance and course combinations on final outcomes.
The findings demonstrate that Apriori-generated rules provide clear, human-understandable insights that can be effectively used for early identification of at-risk students, academic advising, and curriculum planning. Furthermore, the study shows that these interpretable rules can complement existing predictive models and serve as a foundation for developing academic decision support systems.
This research contributes to the field of Educational Data Mining by providing a transparent and practical framework for academic performance prediction in higher education, particularly within data-constrained environments. It highlights the potential of association rule mining as a valuable tool for enhancing evidence-based decision-making in universities.

Prediction, Analytics, Data Mining, Decision Support, Higher Education Prediction, Analytics, Data Mining, Decision Support, Higher Education

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