Using Association Rule Mining to Find the Effect of Course Selection on Academic Performance in Computer Science I

Abstract

It is important for first year students in higher educational institutions to get the best advice and information with regards to course selection and registration. During registration students select the courses and number of courses they would like to enroll into. The decisions made during registration are done with the assistance of academics and course coordinators. This study focuses on the first year Computer Science students and their overall academic performance in first year. Computer Science I has Mathematics as a compulsory co-requisite, therefore after selecting Computer Science I, the students have to enroll into Mathematics and then select two additional courses. Can data mining techniques assist in identifying the additional courses that will yield towards the best academic performance? Using a modified version of the CRISP-DM methodology this work applies an Association Rule Mining algorithm to first year Computer Science data from 2006 to 2012. The Apriori algorithm from the WEKA toolkit was used. This algorithm was used to select the best course combinations with Computer Science I and Mathematics I. The results showed a good relationship between Computer Science I and Biology on its own, Biology with Chemistry and Psychology with Economics. Most of the rules that were produced had good accuracy results as well. These results are consistent in related literature with areas such as Bio-informatics combining Biology and Computer Science.