Personalised Recommendation of PE Network Course Environment Resources Using Data Mining Analysis

Abstract

PE education reform is positively influenced by the creation and use of resources for PE courses as a supplement to and development of traditional teaching strategies. In order to mine the vast amount of data in the network PE curriculum resource system and find useful patterns, this paper uses highly automated DM technology. Additionally, you can forecast users’ upcoming actions and suggest particular course resources to them. This recommendation system makes course resources that users might be interested in based on their browsing history, browsing patterns, and browsing preferences. User registration and login, course retrieval, browsing history, course recommendation, and a module for course scoring are among the system’s primary features. Studies reveal that this method’s recommendation accuracy is up to 96.2 percent, or about 10% higher than that of conventional recommendation methods. The recommendation strategy put forth in this paper has a good recommendation effect, high accuracy, and coverage. It also plays a part in improving the hidden semantic model’s accuracy. For individualised curriculum resource recommendations, this recommendation system offers a good solution.