Analysis of Student Performance using EDM Methods

Abstract

As a student embarks on an educational journey, several factors influence his/her behavior and overall class performance. This paper presents the capabilities of Educational Data Mining (EDM), specifically the use of Association Rule Mining and Pattern Discovery, in the context of a Higher Educational Institute. Real student data comprising of parameters such as class performance, note taking, attention and self-making of assignments have been collected and examined to uncover associations that have been presented in this paper. It was found that student class performance is directly influenced by the attention given to a lecture, proper note-taking and the tendency to self-solve assignments.