Student-at-risk detection by current learning performance indicators using Bayesian networks

TL;DR

This paper proposes a Bayesian network-based predictive model to identify at-risk students in blended learning environments, aiming to enable early intervention and improve student success.

Contribution

It introduces a novel approach using Bayesian networks for student failure prediction in blended learning, validated through empirical studies.

Findings

The model effectively predicts student failures.

Bayesian networks show promise for early warning systems.

Empirical results support practical application in education.

Abstract

The present article is focused on the problem of prediction of student failures with the purpose of their possible prevention by timely introducing supportive measures. We propose a concept for building a predictive model based on Bayesian networks for an academic course or module taught in a blended learning format. Our empirical studies confirm that the proposed approach is perspective for the development of an early warning system for various stakeholders of the educational process.