Clustering for improving educational process mining

Abstract

In this paper, we propose to use clustering to improve educational process mining. We want to improve both the performance and comprehensibility of the models obtained. We have used data from 84 undergraduate students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle's usage summary and/or the students' final marks in the course. Then, we propose to use data from Moodle's logs about each cluster/group of students separately in order to be able to obtain more specific and accurate models of students' behaviour. The results show that the fitness of the specific models is greater than the general model obtained using all the data, and the comprehensibility of the models can be also improved in some cases.