Mining Rare Association Rules from e-Learning Data.

TLDR

Rare association rules, which appear infrequently yet are highly associated with specific data, are especially relevant for imbalanced educational datasets but are difficult to discover with traditional mining algorithms. The study explores extracting rare association rules from Moodle student usage data. The authors extract these rules using specialized mining algorithms tailored to educational datasets. The results compare frequent and rare rule mining algorithms and illustrate discovered rules, demonstrating their performance and usefulness in educational settings.

Abstract

Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare association rules when gathering student usage data from a Moodle system. This type of rule is more difficult to find when applying traditional data mining algorithms. Thus we show some relevant results obtained when comparing several frequent and rare association rule mining algorithms. We also offer some illustrative examples of the rules discovered in order to demonstrate both their performance and their usefulness in educational environments.