Course Content and Learner Classification using Fuzzy Logic to Enhance Adaptive Learning System

Abstract

Adaptive learning systems aim to personalize the learning experience based on individual learner characteristics. These systems can enhance student engagement and improve learning outcomes by tailoring course content to meet each learner's needs. However, the challenge lies in accurately classifying learners and adjusting course materials accordingly. This paper proposes a method for learner classification and course content adaptation using fuzzy logic. By implementing fuzzy logic, the adaptive learning system can categorize learners more precisely based on parameters such as prior knowledge, educational background, and engagement levels. The proposed model enhances the effectiveness of adaptive learning systems by delivering customized learning experiences. Experimental results demonstrate the effectiveness of this approach in optimizing course content and improving learner satisfaction.