Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees

Abstract Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and theeducational scenario is not an exception. However, the usage of the so-called explainable ArtificialIntelligence, even if desirable, is still limited, especially whenever we consider educational datasets.Moreover, the time dimension is not often regarded enough when analyzing such types of data. In thispaper, we have applied the fuzzy version of the Hoeffding Decision Tree to an educational dataset,considering separately STEM and Social Sciences subjects, in order to take into consideration both thetime evolution of the educational process and the possible interpretability of the final results. Theconsidered models resulted to be successful in discriminating the passing or failing of exams at the end ofconsecutive semesters on the part of students. Moreover, Fuzzy Hoeffding Decision Tree occurred to bemuch more compact and interpretable compared to the traditional Hoeffding Decision Tree.