Improving the portability of predicting students performance models by using ontologies

TL;DR

This paper demonstrates that using ontologies with high-level, semantically meaningful attributes enhances the portability of student performance prediction models across different courses in educational data mining.

Contribution

It introduces an ontology-based approach using a taxonomy of student actions to improve model transferability between courses, outperforming low-level attribute methods.

Findings

Ontology-based models improve predictive accuracy across courses.

High-level semantic attributes enhance model portability.

Models trained on one course can be applied to others without accuracy loss.

Abstract

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models excessive dependence on the low-level attributes used to train them, which reduces the models portability. To solve this issue, the use of high level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy.