Graph-Based Recommendation Systems for Personalized Learning Path Optimization in Online Education Platforms
Keywords:
graph-based recommendation, personalized learning paths, online education, knowledge graphs, system architecture, fairness, robustnessAbstract
The rapid expansion of online education platforms has created an urgent need for intelligent systems that can guide learners through increasingly diverse and complex course content. Traditional recommendation algorithms, largely designed for entertainment or e-commerce, fail to capture the sequential dependencies, prerequisite constraints, and heterogeneous interactions that characterize effective learning pathways. This paper presents a comprehensive analysis of graph-based recommendation systems as a foundational approach for personalized learning path optimization in digital education. We argue that graph representation offers a natural formalism for encoding knowledge structures, learner skill states, and content relationships, enabling the design of adaptive pathways that respect pedagogical order. The paper examines systemic trade-offs in graph architecture, including the balance between model expressiveness and computational feasibility, the integration of dynamic graph updates, and the handling of sparse interaction data. Infrastructure considerations are discussed with respect to distributed graph processing, real-time inference pipelines, and deployment on large-scale platforms. Sustainability and robustness are addressed through the lens of concept drift, model retraining strategies, and resilience against adversarial gaming. Fairness and policy implications are scrutinized, focusing on algorithmic bias, equitable access, data privacy, and the need for transparent recommendation logic. Cross-domain comparisons with recommendation systems in social media and retail highlight unique challenges in educational contexts. By synthesizing current research and identifying open challenges, this paper provides a systems-level framework for researchers and practitioners seeking to deploy graph-based learning path optimizers at scale.
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