A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic

TL;DR

This paper introduces a fuzzy logic-based method for constructing and validating learning hierarchies in adaptive educational systems, accounting for the uncertainty in prerequisite relationships between skills.

Contribution

It presents a novel approach applying fuzzy logic to evaluate and refine learning hierarchies based on expert-defined structures and fuzzy prerequisite relationships.

Findings

Effective measurement of relevance degrees in learning hierarchies

Improved validation of prerequisite relationships

Potential for enhanced adaptive learning systems

Abstract

In recent years, adaptive learning systems rely increasingly on learning hierarchy to customize the educational logic developed in their courses. Most approaches do not consider that the relationships of prerequisites between the skills are fuzzy relationships. In this article, we describe a new approach of a practical application of fuzzy logic techniques to the construction of learning hierarchies. For this, we use a learning hierarchy predefined by one or more experts of a specific field. However, the relationships of prerequisites between the skills in the learning hierarchy are not definitive and they are fuzzy relationships. Indeed, we measure relevance degree of all relationships existing in this learning hierarchy and we try to answer to the following question: Is the relationships of prerequisites predefined in initial learning hierarchy are correctly established or not?