Personalised and Collaborative Learning Experience (PCLE) Framework for AI-driven Learning Management System (LMS)

Background Understanding student engagement and academic performance is crucial in AI-driven e-learning environments. Many learning management systems (LMS) lack effective collaborative course recommendation strategies, limiting support for personalised learning experiences. Methods This study developed and evaluated collaborative filtering and machine learning models to generate course recommendations. Machine learning models such as K-Nearest Neighbours (KNN), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF) were applied. Two education-related datasets from Kaggle were used. The first contains 100,000 course reviews from Coursera, and the second dataset includes 209,000 course details and comments from Udemy. Data preprocessing was conducted to clean and structure both datasets. The model effectiveness was evaluated using Mean Absolute Error (MAE), Hit Rate (HR), and Average Reciprocal Hit Ranking (ARHR). Results K-Nearest Neighbours showed the highest performance on the Coursera dataset, while Singular Value Decomposition and Neural Collaborative Filtering maintained stable predictive accuracy across both datasets. The findings indicate that dataset characteristics influenced model performance. K-Nearest Neighbours worked effectively with structured and consistent data, while Singular Value Decomposition and Neural Collaborative Filtering produced consistent outcomes across diverse datasets. Conclusions This study contributes to e-learning research by demonstrating the potential of collaborative filtering and machine learning in enhancing course recommendations and promoting engagement in the learning management system. Limitations include the use of two datasets and a limited set of machine learning models. Future work aims to integrate learning styles and evaluate the framework across more diverse educational contexts to support adaptive and collaborative learning.