Abstract

Individualized learning is a crucial aspect in enhancing learner interaction and performance in online learning systems. Conventional forms of learning are not always dynamic with regard to the needs of particular learners. The Hybrid Neuro-Fuzzy Adaptive Model is an integration of neural networks and fuzzy logic to streamline the learning paths in real-time to facilitate the learning process. The purpose of the study is to assess the success of the Hybrid Neuro-Fuzzy Adaptive Model in enhancing the learning results and the customer satisfaction in the e-learning setting. The model was deployed in a web-based learning system, which handles real-time learner information, performance, engagement, and affective state. The neural network evaluated the learning interactions between learners and made predictions of the next step of learning, and the fuzzy logic varied the direction according to engagement and emotional condition. The model was contrasted with the conventional learning path techniques with regard to the rate of task completion, test scores, and student response. Students with the Hybrid Neuro-Fuzzy Model increased test scores by 15% and could complete the tasks 95% as opposed to 70% in conventional systems. The engagement of learners rose by 20%, and 92% of learners said that they were highly satisfied with the personalized learning experience. The Hybrid Neuro-Fuzzy Adaptive Model helps a great deal with the learning performance and the involvement of the learning participants. The potential that it can be implemented on a large scale is founded on its real-time flexibility and customized learning maps. The next generation of research should aim at making it scalable and more efficient.