A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic
This work addresses the need for more accurate and flexible learning hierarchy construction in adaptive educational systems, though it appears incremental by applying fuzzy logic to an existing expert-defined framework.
The paper tackles the problem of constructing learning hierarchies in adaptive learning systems by addressing that prerequisite relationships between skills are fuzzy, not definitive. It proposes a fuzzy logic approach to measure the relevance degree of these relationships and evaluate if the predefined prerequisites are correctly established.
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?