Construction and Innovative Application of Fundamental Training Educational Resource Repository Using Data Mining Techniques

Abstract

With the rapid advancement of information technology and data mining methodologies, the educational sector is undergoing an unprecedented transformation. The increasing proliferation of online educational resources offers significant advantages for educators and learners, yet presents challenges in resource retrieval and recommendation. Existing query and recommendation systems frequently rely on simplistic keyword matches and browsing history, struggling to cater to the escalating demand for personalized learning experiences. This study introduces an innovative method for constructing a Fundamental Training Educational Resource Repository (FTERR) leveraging data mining techniques. Firstly, students’ learning needs are intricately captured using knowledge graph and semantic analysis methods. Secondly, addressing the challenges of resource storage and retrieval, a novel data storage model is proposed, which, when paired with cutting-edge clustering algorithms, facilitates rapid and visualized retrieval of educational materials. This research not only furnishes novel methodologies for data mining applications in the educational domain but also fosters the advancement of educational informatization.