Predicting Students' Performance: Incremental Interaction Classifiers

Abstract

One of the Educational Data Mining (EDM) main aims is to predict the final student's performance, analyzing their behavior in the Learning Management Systems (LMSs). Many studies make use of different classifiers to reach this goal, using the total interaction of the students. In this work we study if it is possible to build more accurate classification models in order to predict the output, analyzing the interaction in an incremental way. We study the data gathered for two years with three kinds of classifying algorithms and we compare the total interaction models with the incremental interaction models.