Analysis of Academic Results for Informatics Course Improvement Using Association Rule Mining

In this chapter we analyze the application of association rule mining for assessing student academic results and extracting recommendations for the improvement of course content. We propose a framework for mining educational data using association rules, and a novel metric for assessing the strength of an association rule, called "cumulative interestingness". In a case study, we analyze the Informatics course examination results using association rules, rank course topics following their importance for final course marks based on the strength of the association rules, and propose which specific course topic should be improved to achieve higher student learning effectiveness and progress.