Performance Evaluation of Naive Bayes and Decision Stump Algorithms in Mining Students' Educational Data

Abstract

Educational data mining is an emerging trend, concerned with developing methods for exploring the huge data that come from the educational system. This data is used to derive the knowledge which is useful in decision making which is known as Knowledge Discovery in Databases (KDD). EDM methods are useful to measure the performance of students, assessment of students and study students’ behavior etc. In recent years, Educational data mining has proven to be more successful at many of the educational statistics problems due to enormous computing power and data mining algorithms. The main objective of this research is to find out interesting patterns in the educational data that could contribute to predicting student performance .This paper describes how to apply the main data mining methods such as prediction and classification to educational data.