Student Performance Prediction Using Educational Data Mining Techniques

Abstract

Educational sector produces data in large amount that is too voluminous and complex to understand. There is a need to efficiently filter and prioritize the data so as to deliver the relevant information to get rid of information overloading. Data mining searches through the large amount of dynamically generated data to present users with the useful and understandable patterns and trends. It has the power to use the raw data effectively which has been produced by universities, to draw the hidden patterns and the relationships among the attributes that are used in predicting the student performance, their behaviour effectively. In this paper the data mining techniques have been briefly described. The literature review of educational data mining is also done. This paper, implements data mining techniques such as Naive bayes and Support vector machine to predict the student performance.