An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree

Hedayetul Islam,Mahfuza Haque

doi:10.14569/ijacsa.2012.030824

Abstract

Improving student’s 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 student’s GPA and based on that instructor can take necessary step to improve student academic performance

Highlights

Graded Point Average (GPA) is a commonly used indicator of academic performance
Many factors could act as barriers to student attaining and maintaining a high GPA that reflects their overall academic performance, during their tenure in university
With the help of clustering algorithm and decision tree of data mining technique it is possible to discover the key characteristics for future prediction

Summary

INTRODUCTION

Graded Point Average (GPA) is a commonly used indicator of academic performance. Many universities set a minimum GPA that should be maintained. With the help of clustering algorithm and decision tree of data mining technique it is possible to discover the key characteristics for future prediction. The main goal of clustering is to partition students into homogeneous groups according to their characteristics and abilities (Kifaya, 2009). These applications can help both instructor and student to enhance the education quality. This study makes use of cluster analysis to segment students in to groups according to their characteristics and use decision tree for making meaningful decision for the student’s. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Ai; If Si is empty Attach a leaf labeled with the most common class in samples; Else attach the node returned by generate-decisiontree (Si,attribute-list-attribute); Each internal node tests an attribute, each branch corresponds to attribute value, and each leaf node assigns a classification

Decision Tree

AND DISCUSSION

CONCLUSION AND FUTURE WORK