Data Mining Applications Used in Education Sector

Abstract

The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification, followed by clustering and association. Further, this research also contains the study conducted on moodle data to find anomalies. K-means clustering was applied to find the optimal number of clusters on moodle data that consists of log and quiz dataset. The growth in the number of Internet users has increased learning through the online process. Hence, several activities are performed in OL systems, which generate a massive amount of data to be analysed to obtain useful information. Therefore, this type of research is very beneficial to academicians and instructors to identify the learner’s behaviors and develop suitable models.