A Survey on Educational Data Mining using Association Rule Mining

Abstract

In data mining, the knowledge is extracted from the large data sets and the result is generated from various data mining techniques. Here, we used educational data mining for improving graduate students’ performance. The most popular application of data mining in education is to predict student performance. The Educational Data Mining (EDM) spotlights on modeling and evaluate student’s performance based on scores in an examination. Different techniques like neural networks, association rules, regression, Bayesian networks, and rule-based systems are applied to study the educational data. Association rules are an essential part of every research field. In this perception, we use association rule mining techniques for discovering relations between variables in large databases. Various algorithms are available for association rule mining. Apriori algorithm can be used with WEKA tool to extract the set of rules and scrutinize the given data to classify the student based on their academic performance.