An Approach of Improving Students Academic Performance by using k means clustering algorithm and Decision tree

TL;DR

This paper proposes a hybrid data mining approach combining k-means clustering and decision trees to predict students' GPA, aiming to help educators improve academic performance and reduce dropout rates.

Contribution

It introduces a novel hybrid method integrating clustering and decision trees for predicting student GPA, aiding targeted academic interventions.

Findings

Effective prediction of student GPA using the hybrid model

Potential to reduce dropout rates through early intervention

Improved academic performance by tailored support

Abstract

Improving students academic performance is not an easy task for the academic community of higher learning. The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average,GPA in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment lab work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. In this paper, we present a hybrid procedure based on Decision Tree of Data mining method and Data Clustering that enables academicians to predict students GPA and based on that instructor can take necessary step to improve student academic performance.