Research and Design of Intelligent Classroom Technology Adoption Willingness Analysis Method and System Based on Data Mining and Recommendation Algorithms

This inquiry delves into the design and implementation of a system for analyzing the willingness to adopt intelligent classroom technology, grounded in data mining and recommendation algorithms. As information technology burgeons, intelligent classroom technology plays an increasingly pivotal role in contemporary education. However, fluctuation exists in educators' willingness to embrace these technologies. This study constructs an analytical system aimed at augmenting educators' adoption willingness. Employing an array of data mining techniques, such as cluster analysis and relationship mining, along with item-based collaborative filtering algorithms, it adeptly handles and scrutinizes a voluminous dataset of educator willingness. The findings reveal that these methodologies effectively identify key factors influencing educators' adoption willingness and offer tailored strategies and recommendations. The system's design emphasizes user-friendliness, real-time data updates, and stringent data security and privacy safeguards. Ultimately, this system not only enhances the adoption rates of educational technology but also significantly contributes to elevating the quality of education.