Optimising Educational Performance through Clustering Algorithm Evaluation

Abstract

Analyzing student performance is a vital undertaking within the realm of educational data mining (EDM). This process empowers academic institutions to uncover significant trends, pinpoint students who may be struggling, and formulate effective support strategies. This scholarly article delves into the application of clustering methodologies to classify students based on various performance metrics, such as academic grades, attendance records, engagement levels, and involvement in extracurricular activities. By segmenting students into distinct groups, educators can gain a clearer understanding of their behavioural characteristics, optimize resource distribution, and deploy customized intervention programs.

This research paper presents an evaluative comparison of diverse clustering approaches utilized for assessing student academic outcomes. The investigation evaluates the efficacy of several prominent clustering algorithms, including K-Means, DBSCAN, BIRCH, and Expectation Maximization (EM), in classifying students according to their educational achievements. The findings illuminate the advantages and limitations of each method, offering valuable perspectives on their practical utility in the field of educational data mining. The substantial increase in educational data has necessitated the adoption of sophisticated data mining techniques to extract meaningful patterns and actionable intelligence. Clustering, an unsupervised machine learning paradigm, is extensively employed to categorize students based on their performance, thereby assisting educators in identifying vulnerable students and customizing appropriate interventions.