The Analysis of Student Learning Behavior Based on Teachermate Data

In the era of big data, thanks to the development of digital information technology (e.g., data mining), the concept of the smart campus has emerged and changed the traditional education model. However, the smart education products, mainly the teachermate platform, cannot provide algorithm-based personalized education services for teachers and students by targeting and analyzing student data. Based on this background, this paper takes some students as the sample, and uses the data of various learning behavior characteristics on the teachermate platform and the final exam paper results of the student grade management system on the teacher's side as the data source, and analyzes the data through data mining algorithms. The final results of the analysis were obtained, and suggestions for optimization of the platform based on academic alerts were obtained for faculty, teachers, and the teachermate platform.