Multi-source Relations for Contextual Data Mining in Learning Analytics

TL;DR

This paper proposes a method for mining meaningful and interpretable patterns from heterogeneous multi-source educational data to enhance learning analytics and support students' academic progress.

Contribution

It introduces low-complexity pattern mining algorithms that handle data heterogeneity and interdependency across multiple educational sources.

Findings

Effective pattern extraction from diverse data sources.

Patterns are meaningful and directly usable for students.

Reduced computational complexity in multi-source data mining.

Abstract

The goals of Learning Analytics (LA) are manifold, among which helping students to understand their academic progress and improving their learning process, which are at the core of our work. To reach this goal, LA relies on educational data: students' traces of activities on VLE, or academic, socio-demographic information, information about teachers, pedagogical resources, curricula, etc. The data sources that contain such information are multiple and diverse. Data mining, specifically pattern mining, aims at extracting valuable and understandable information from large datasets. In our work, we assume that multiple educational data sources form a rich dataset that can result in valuable patterns. Mining such data is thus a promising way to reach the goal of helping students. However, heterogeneity and interdependency within data lead to high computational complexity. We thus aim at designing low complex pattern mining algorithms that mine multi-source data, taking into consideration the dependency and heterogeneity among sources. The patterns formed are meaningful and interpretable, they can thus be directly used for students.