A Unified Framework for Personalized Learning Pathway Recommendation in E-Learning Contexts

Generating personalized learning pathways for e-learners is a critical issue in the field of e-learning as it plays a pivotal role in guiding learners towards the successful achievement of their learning objectives. The existing literature has proposed various methods from different perspectives to address this issue, including learner-based, knowledge-based, and hybrid recommendation approaches. Among these, hybrid recommendation approaches have shown significant potential in generating highly personalized and logically structured learning pathways by combining the advantages of both learner-based and knowledge-based recommendation methods. However, there is a lack of a unified learning pathway recommendation framework that comprehensively incorporates essential parameters related to learners, learning objects, and domain knowledge. To overcome these challenges, we propose a unified framework to address the personalized learning pathway recommendation problem. In this framework, we develop a novel two-hierarchy modeling architecture that comprehensively formulates the requirements of the problem. Additionally, we present a Modified Ant Colony Optimization Algorithm to effectively discover the optimal learning pathways tailored to meet the diverse requirements and preferences of e-learners. To evaluate the effectiveness of the proposed method, we carry out extensive computational experiments on 12 simulation datasets of varying sizes and complexity levels. The computational results demonstrate that our proposed method outperforms other competing methods in terms of optimization performance and stability. Furthermore, we conduct an empirical study to verify the effectiveness of our method in a real-world learning scenario. The results obtained from this study show that our method effectively generates high-quality personalized learning pathways, thereby enhancing the learning experiences and outcomes of e-learners.