A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory

Abstract

Personalized learning is a defining characteristic of current education, with flexible and adaptable experiences that respond to individual learners’ requirements and approaches to learning. Traditional implementations of educational theories—such as Kolb’s Experiential Learning Theory—often follow rule-based approaches, offering predefined structures but lacking adaptability to dynamically changing learner behavior. In contrast, AI-based approaches such as artificial neural networks (ANNs) have high adaptability but lack interpretability. In this work, a new model, a fuzzy-ANN model, is developed that combines fuzzy logic with ANNs to make recommendations for activities in the learning process, overcoming current model weaknesses. In the first stage, fuzzy logic is used to map Kolb’s dimensions of learning style onto continuous membership values, providing a flexible and easier-to-interpret representation of learners’ preferred approaches to learning. These fuzzy weights are then processed in an ANN, enabling refinement and improvement in learning recommendations through analysis of patterns and adaptable learning. To make recommendations adapt and develop over time, a Weighted Sum Model (WSM) is used, combining learner activity trends and real-time feedback in dynamically updating proposed activity recommendations. Experimental evaluation in an educational environment shows that the model effectively generates personalized and changing experiences for learners, in harmony with learners’ requirements and activity trends.