Enhancing Academic Decision making at Higher Educational Institutions using Classification and Clustering Techniques

Abstract

The main objective of higher education institutions is to provide quality education to its students. Student`s performance prediction is essential to be conducted for a university to prevent student fail. This paper represents the data mining techniques used for analysing student’s performance. Educational institutions contain an enormous amount of academic database containing student details. These student databases along with other attributes are taken into consideration like family background, family income, etc. It will help us by identifying promising students and by providing us a chance to pay heed and to refine those students who likely get low marks. For answer, we prepare a structure which will analyse the student’s performance from their last performances using concepts of Data Mining under Classification and Clustering techniques like Decision Tree, Neural Network and K-Means Clustering Algorithm. By these techniques we extract knowledge that describes students’ performance in end semester examination.