Clustering Student Performance: A Data-Driven Approach to Monitor Academic Success

Abstract

The goal of Educational Data Mining (EDM) is the exploration of hidden patterns and insights in educational data. Making use of the EDM approach of clustering, this research explores the analysis of variation in students performance across the course of an academic degree. We perform experiments on the data of 210 students belonging to the Department of Software Engineering in an attempt to discover patterns between three class of learners’ – high performers, intermediate performers, and low performers. These patterns are not only analyzed across different learner classes but also across different genders. The research also makes use of heatmap analysis to highligh subject-wise performance and to better understand the subjects that students struggle in. The findings of the reseach highlight the subjects that students have difficulties in and show that although students in most instances performed well in theoretical courses, several students had difficulty in practical courses. A comparison between two batches revealed that Batch-02 had generally improved performance which was particularly evident in the sixth semester of the degree program. These findings provide an alternative understanding of the intricate interaction between academic performance and student behaviors, which can be invaluable in guiding educators and policymakers to devise interventions that could help students achieve better results and ultimately reshape the learning paradigm.