Abstract

Abstract: Applicationof data mining and machine learning techniques in the field of education isgaining prominence these days as it would help Identify weak areas of thestudent and predict student performance. This would help both the student andthe educational institution. Research till date has only predicted the finalgrade of the student. In this paper, we attempted to analyze the variousfactors that contributed to the student performance like the background of thestudent, involvement in social activities, student performance in thecoursework etc. data mining techniques such Decision Tree J48, Naïve Bayesian andKNN algorithms are used to predict the student performance in mathematics atsecondary school. We find that the background of the student and involvement inthe social activities have played an important role in forecasting studentperformance. This model could be adopted for the early prediction of studentachievements and assist in improving on the weak areas of the student.

Keywords:EducationalData Mining, Classification