Application of k Means Clustering algorithm for prediction of Students Academic Performance

TL;DR

This paper presents a k-means clustering approach to analyze and monitor students' academic performance, aiding academic decision-making in higher education.

Contribution

It introduces a combined clustering and deterministic model for analyzing student results, providing a new method for performance monitoring.

Findings

Effective grouping of student scores based on performance levels

Enhanced decision-making for academic planning

Potential for scalable performance analysis in institutions

Abstract

The ability to monitor the progress of students academic performance is a critical issue to the academic community of higher learning. A system for analyzing students results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k mean clustering algorithm for analyzing students result data. The model was combined with the deterministic model to analyze the students results of a private Institution in Nigeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners.