Personalised course recommender in a virtual reality learning environment

Abstract

The rapid shift towards online education during the COVID‐19 pandemic has catalysed significant changes in the educational landscape, propelling the rise of e‐learning. This modality supports individualised pacing and convenience, promotes lifelong learning, and is more cost‐effective compared to traditional classroom‐based instruction. Despite its benefits, e‐learning presents challenges, mainly in navigating the vast array of online resources. Learners often find it difficult to locate educational materials that match their specific needs. Current recommendation systems, largely dependent on keyword‐based inputs, fail to deliver personalised content that resonates with individual preferences and learning objectives. Additionally, the transition to online platforms does not fully address issues of engagement and personalisation, critical in traditional learning environments, leading to potential learner disengagement due to insufficient interactivity and customisation. This study introduces a novel solution: a Personalised Course Recommendation System (PCRS) integrated within a Virtual Reality Learning Environment (VRLE). Utilising a blend of content‐based and collaborative filtering, enhanced by Natural Language Processing (NLP) techniques and Machine Learning (ML) algorithms, this system aims to provide tailored course suggestions. These recommendations are based on the user profiles and projected course completion success, which aligns with the existing knowledge of learners and the evolving educational needs of the learners. The VRLE is designed to adapt interactively to the chosen courses, offering immersive, three‐dimensional scenarios that encourage active engagement and deep learning. This approach not only addresses the limitations of existing e‐learning platforms by enhancing personalisation and interactivity, but also significantly improves the engagement and retention of learners. In conclusion, by embedding a sophisticated, adaptive recommendation system within a VRLE, this study seeks to revolutionise the e‐learning experience, fostering improved comprehension and sustained engagement among learners. The results indicate a promising improvement in educational delivery and student satisfaction.