Online Learning Resource Recommendation Method Based on Learner Model

In the era of burgeoning digital educational resources, tailoring personalized learning experiences for individuals has emerged as a paramount concern. This paper delineates the development of an innovative online learning resource recommendation system, underpinned by an advanced learner model. The study leverages a gamut of data mining methodologies, encompassing both machine learning and user behavior analytics, to craft learner models of high personalization. These models intricately consider various facets such as the learner’s prior knowledge, learning style, interests, and historical learning interactions. Central to our system is a sophisticated recommendation algorithm. This algorithm amalgamates decision tree methodologies with state-of-the-art natural language processing techniques, effectively sifting through an extensive corpus of online learning materials to pinpoint resources that resonate with individual learner profiles. The system’s efficacy was rigorously tested across multiple online learning platforms. Empirical results from these tests unequivocally demonstrate that our system surpasses conventional recommendation approaches, particularly in augmenting learner engagement and satisfaction. This research contributes a novel paradigm to the personalized recommendation of online learning resources. Moreover, it furnishes invaluable insights into the ongoing evolution of educational technologies, marking a significant stride in the realm of digital learning.