Knowledge Discovery From Academic Data Using Association Rule Mining

Discovering the hidden knowledge from large volume of educational data and applying it properly for decision making is essential for ensuring high quality education in any academic institution. This knowledge is extractable through data mining techniques. Association Rule Mining technique aims at discovering implicative tendencies that can provide valuable information for the decision maker. In this paper, we present an applied research on mining Association Rule using academic data of a university. We have discovered knowledge regarding the academic performance and personal statistics of students. Here we have developed a technique to transform the existing relational database for students' academic performance into a universal database format using academic and personal data of a student. After that we have transformed the universal format into a modified format for suitability of using Association Rule mining algorithm. We have used Apriori algorithm for finding interested association rules from the transformed database which can be useful to extract knowledge of students' academic progress, decay in their potentiality, abandonment as well as retention of students. The impact of courses and curriculum and teaching methodologies are also found from the extracted knowledge which is beneficial for any institution of higher education.