An Integrated Recommendation System for Customised E-Learning Using the BERT Model

Abstract

Digitization of education has made education more accessible. With the growing accessibility, the primary challenge for e-learning is to customize the learning environment to the needs and preferences of the learners. The learning can be customized by considering features such as persona type, skill level, learning goal, learning style, educational background, past knowledge, and memory span of learners. The tailored learning environments enhance learner engagement and significantly increase the number of learners who successfully achieve their educational goal. This work presents a recommendation system using the Case-Based Reasoning (CBR) and the Rule-Based Reasoning (RBR) with the Bidirectional Encoder Representations from Transformers (BERT) model embedded for sentence classification. The proposed integrated system has an F1 score of 0.74, indicating the balance between making correct and useful recommendations, and a higher normalized Discounted Cumulative Gain (nDGC) of 0.81 shows that the system ranks the most relevant learning modules at the top of the recommendation list. The classification of learning objectives using the BERT model into predefined domains achieved an accuracy of 95%. The system results in a structured learning path, which is more organized and engaging. The system is beneficial to novice learners, as it reduces failure rates and improves completion time of the learning process.