Research on information retrieval and knowledge discovery in future learning center with virtual reality technology

Abstract

To enhance user experience, search accuracy, and retrieval speed, ensuring easier access to required information for learners, this study utilized the K-means algorithm to gather extensive behavioral data from students’ virtual learning environments, including browsing, searching, and interaction information. An information retrieval model was subsequently developed, employing a vector space model to represent documents and queries. Document clustering was performed using the K-means algorithm to boost retrieval efficiency. Additionally, student clustering through the K-means algorithm led to the creation of student profiles, enabling a more precise understanding of their academic interests and needs. In terms of knowledge recommendation, a personalized recommendation process was designed. By analyzing students’ historical behaviors, a model of their knowledge interests was established, and based on this model, a tailored knowledge recommendation strategy was formulated for each student. The study results indicated that the search accuracy of virtual reality technology reached 93%, with an average response time of 2.5 seconds, surpassing traditional learning environments. This demonstrates that the proposed method provides a valuable reference for information retrieval and knowledge discovery in future learning centers.