Competence-Based Student Modelling with Dynamic Bayesian Networks

TL;DR

This paper introduces a method to construct dynamic Bayesian network student models using competence maps, enabling effective monitoring of competence development in online education.

Contribution

It presents a novel approach to build student models from competence maps with defined relationships, applied to Mexican high school competences.

Findings

Model successfully traces competence development in hypothetical students.

Method shows potential for monitoring real students' progress.

Demonstrates applicability to online educational settings.

Abstract

We present a general method for using a competences map, created by defining generalization/specialization and inclusion/part-of relationships between competences, in order to build an overlay student model in the form of a dynamic Bayesian network in which conditional probability distributions are defined per relationship type. We have created a competences map for a subset of the transversal competences defined as educational goals for the Mexican high school system, then we have built a dynamic Bayesian student model as said before, and we have use it to trace the development of the corresponding competences by some hypothetical students exhibiting representative performances along an online course (low to medium performance, medium to high performance but with low final score, and two terms medium to high performance). The results obtained suggest that the proposed way for constructing dynamic Bayesian student models on the basis of competences maps could be useful to monitor competence development by real students in online course.