Concept Graph-Based Learning Object Recommendation for Enhanced Educational Content Delivery
Author(s):
Akhil Chaturvedi1* and Taranveer Singh2
The exponential growth of online educational resources has created both opportunities and challenges for learners and educators. This paper presents a novel approach to learning object recommendation using a concept graph framework. We address the challenges of information overload and personalized learning by developing a system that leverages diverse data sources, including 3,785 Wikipedia articles, 12 machine learning textbooks, and 1,914 video transcripts, to construct a comprehensive concept graph. Our method employs advanced natural language processing techniques, including Word2Vec, Smooth Inverse Frequency (SIF), and contextual SIF embeddings, to create a rich representation of educational concepts and their relationships. We introduce a new recommendation algorithm that utilizes this concept graph to provide personalized and contextually relevant educational content to learners. The system is evaluated using the CiteULike dataset, comprising 5,551 users, 16,980 articles, and 204,986 user-item interactions. Our approach demonstrates significant improvements over baseline methods, achieving a recall@50 of 0.27, compared to 0.18 for collaborative topic modeling and 0.12 for popularitybased recommendation. Furthermore, we explore the implications of our concept graph-based system for enhancing educational content delivery, addressing the cold start problem in recommendation systems, and improving the interpretability of recommendations for both learners and instructors. Our findings suggest that this approach offers a promising direction for advancing personalized learning experiences in online environments and contributing to the development of more effective educational technologies
