A study on student-oriented personalized diversified teaching model driven by artificial intelligence
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Mengling Ma, Hao Zhang, Chengzhu Li, Hao Cu, Wenwen Zhang
This essay seeks to construct a varied teaching methodology and to support students' individualized growth. Collaborative filtering recommendation method is used to examine students' interests and pastimes by looking at the framework of intelligent recommendation system. The collaborative filtering algorithm, process of algorithm input, algorithm processing and algorithm output are analyzed in detail. According to the results, the customized teaching mode has a better advantage over the traditional teaching mode in terms of discussion time, classroom engagement, activity form, and learning content, which are correspondingly 20%, 19%, 40%, 30%, and 10% higher. 70% of the content in a course is process-based in the conventional teaching approach, which is more rigid, less flexible, and extensively structured. The personalized teaching mode, on the other hand, pays more attention to the flexibility of teaching, with a degree of flexibility of 60%.
Intelligent recommendation; User-based collaborative Filtering; Multiple teaching models; Adjacent content; Directional clustering