Applying the Apriori Algorithm in Educational Data Mining to Evaluate Student Performance and Enhance Academic Productivity

Abstract

This study presents an applied case study in Knowledge Discovery using data mining techniques. In order to discover significant patterns in the students' academic data at King Khalid University from 1434 AH to 1437 AH. And find out general indicators on academic performance to support decision makers to set the educational policies at the university.
This research applied the CRIP DM methodology, which is a popular data mining application on the sample data. R package was used for performing the statistical analysis of academic indicators, and the Apriori Algorithm in Weka mining tool was also used.
This study demonstrates that there is a set of Patterns that can give key indicators, such as the constancy of some grades for students in some of the semesters, an increase in grades up to certain semesters as well as a decrease in others, in addition to the variance analysis of students grades. Moreover, patterns discovery in students' grades in some semesters.
Furthermore, this study came out with a set of recommendations that would contribute to the quality and success of the educational process, and importantly is the need for developing an integrated digital data warehouse that will be the core for future researches.
Keywords: Data Mining, Apriori Algorithm, King Khalid University.