Application of Data Mining Model in Smart Chemistry Education

Abstract

In order to solve the problem that the activities of teachers and students in traditional teaching methods are constrained by lesson plans, the teaching methods are aging, and they do not have the ability to expand students’ learning through big data; due to the problem of low self-learning and extracurricular communication ability of students after class, the author proposes the application of a data mining model in smart chemistry education. The author designs an educational wisdom platform based on big data, and its overall structure includes physical layer, virtual resource layer, logic layer, presentation layer, application layer, network layer, and user layer; the big data center module in the platform collects all business data through devices such as networks and sensors and stores the business data in mass data storage devices; the software design part uses the multi-feature fusion acquisition algorithm to collect student data and completes the early warning of student performance through the performance early warning algorithm based on correlation analysis technology. The results showed that only 5.15% and 4.49% of the students who designed the platform could not improve their after-class autonomous learning and extracurricular communication skills, and more than 95% of the students had good feedback after using the platform. Conclusion. It shows that the design platform can effectively improve the students’ ability of self-learning and extracurricular communication after class, and the application efficiency is high.