Dropout through extended association rule netwoks: A complementary view

Abstract

Dropout is a critical problem that has been studied by data mining methods. The most widely used algorithm in this context is C4.5. However, the understanding of the reasons why a student dropout is a result of its representation. As C4.5 is a greedy algorithm, it is difficult to visualize, for example, items that are dominants and determinants with respect to a specific class. An alternative is to use association rules (ARs), since they exploit the search space more broadly. However, in the dropout context, few works use them. (Padua et al., 2018) proposed an approach, named ExARN, that structures, prunes and analyzes a set of ARs to build candidate hypotheses. Considering the above, the goal of this work is to treat the dropout problem through ExARN as it provides a complementary view to what is commonly used in the literature, i.e., classification through C4.5. As contributions we have: (a) complementary views are important and, therefore, should be used more often when the focus is to understand the domain, not only classify; (b) the use of ARs through ExARN may reveal interesting correlations that may help to understand the problem of dropping out.