An Application of Association Rule Mining in Analyzing Courses Affecting the Results of Financial Mathematics Case: UITM Seremban Campus

Abstract

Introduction to Financial Mathematics (FM) is one of the subjects that is compulsory to be seated by all students of Diploma in Actuarial Science in UiTM; and is also known as a "killer subject" among the students. Due to this, lecturers are facing problems of high failure rate which exceeds the UiTM's Key Performance Indicator (KPI) by more than 25% of the students. Thus, the objective of this study is to analyse the courses affecting the results of FM course among the students by using Association Rule Mining. The analysis was carried out only for students who are taking this course for the first time. Out of 360 data gathered, only 273 datasets were employed for the purpose of conducting this study. Results showed that 128 rules were developed and after removing the redundancy, surprisingly 62 interesting rules were found to represent the relationship between all courses and the passing of FM.