Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling
Christos Troussas, Akrivi Krouska, Phivos Mylonas, Cleo Sgouropoulou · 2023
Personalized approaches and tailored support have become increasingly significant in the field of online education, aiming to enhance the overall learning experiences of learners. This paper introduces a novel approach for addressing challenges in providing tailored support by utilizing chatbot technology and the flexibility of fuzzy logic. The chatbot is responsible for delivering precise and tailored responses to learners, considering their input, typically in text form. This is accomplished through the utilization of a rule-based system that is capable of generating accurate answers according to predefined criteria. To augment this support, fuzzy logic is employed for modeling the learners' knowledge, thereby enhancing the chatbot's proficiency in accurately evaluating and responding to inquiries. Consequently, the provision of assistance can be tailored to the specific knowledge level of learners, aiding them in achieving their educational goals. This methodology is incorporated in an intelligent tutoring system designed to provide tutoring for the programming language Java. The evaluation findings demonstrated the effectiveness of our approach in delivering personalized assistance through a chatbot. The results indicated that the chatbot's responses were highly rated in terms of clarity, relevance, and usefulness. Additionally, the system was found to effectively address learners' needs with quality and adequacy.