Application of data mining in educational databases for predicting academic trends and patterns

Abstract

Data mining is a process of identifying and extracting hidden patterns and information from databases and data warehouses. There are various algorithms and tools available for this purpose. Data mining has a vast range of applications ranging from business to medicine to engineering. In this paper, we discuss the application of data mining in education for student profiling and grouping. We make use of Apriori algorithm for student profiling which is one of the popular approaches for mining associations i.e. discovering co-relations among set of items. The other algorithm used, for grouping students is K-means clustering which assigns a set of observations into subsets. In the field of academics, data mining can be very useful in discovering valuable information which can be used for profiling students based on their academic record. We apply Apriori algorithm to the database containing academic records of various students and try to extract association rules in order to profile students based on various parameters like exam scores, term work grades, attendance and practical exams. We also apply K-means clustering to the same set of data in order to group the students. The implemented algorithms offer an effective way of profiling students which can be used in educational systems.