Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject

Abstract

This work addresses the problem of inferring student performance from information acquired in a Learning Management System (LMS). In particular, we explore the capabilities offered by Moodle, a widely used LMS. The study is performed on data acquired from four classes within the same subject, for whom we make inferences about student performance marks. The developed methodology describes the degree in which the acquired information allows to predict the student marks belonging to the continuous evaluation, while the prediction of the final students marks has a higher intrinsic complexity that would require a more in- depth study of the relevant variables. Then, we follow a fully data-driven process to discover similarities among classes. In particular, we propose the use of a dependence estimation measure, the normalized Hilbert-Schmidt Independence criterion. We show how this dependence measure is useful to determine relations among classes, based on their particular teaching methodology, by only using data acquired from the LMS. This opens the door to explore the capabilities of LMS in similarity search between offered courses along an educational platform. In order to help the community and serve as a common way of comparison, we provide the source code of the proposed methodology.