Mining Rare Association Rules from e-Learning Data

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.