Discovering the Collaborative Filtering (CF) Recommender System for Resource Selection in a Moodle Learning Environment

Abstract

This paper investigates the feasibility and potential benefits of implementing a Collaborative Filtering (CF) Recommender System within a Moodle Learning Environment. With the rapid proliferation of e-learning platforms, the integration of sophisticated recommender systems is becoming increasingly critical to mitigate the challenges associated with resource discovery. The CF system capitalizes on the collective intelligence encapsulated in user interactions and preferences to provide personalized resource recommendations. This paper elucidates a comprehensive use case scenario of deploying user-based collaborative filtering algorithms within the Moodle infrastructure, specifically emphasising the memory-based process. Furthermore, the study delineates the potential implications of assimilating a CF system, including facilitating personalised learning, enhancing user engagement, optimising resource discovery, and promoting inclusive learning. Future research trajectories encompass refining the underlying algorithms, addressing ethical and privacy considerations, amalgamating with other emergent technologies, assessing system effectiveness, and optimizing user interface and user experience.