Research on Personalized Teaching Recommendation System Based on Big Data from Educational Platforms

DOI: https://doi.org/10.62517/jike.202604123

Author(s)

Mengying Lu*, Kai Cheng

Affiliation(s)

School of Computer and Artificial Intelligence, Henan University of Finance and Economics, Zhengzhou, China *Corresponding Author

Abstract

There are now thousands of courses available on online learning platforms. Increased options are better but can be too much to handle students. Most people find it difficult to identify exactly what suits them. To solve this problem, we created a personalized course recommendation system. Spring Boot is used as the backend, Vue.js as the frontend, and MySQL as the database in this system. Two strategies have been combined. Firstly, it identifies users with like-minded interests. Secondly, it incorporates famous courses that are well-rated by most learners. Combined, these techniques enhance relevance and coverage. The platform is compatible with various forms of learning materials such as video courses, reading lists, and case studies. Progress of users can be seen on the intuitive dashboards. They have the opportunity to connect and exchange ideas using a built-in community space. During the testing process, all new users were given helpful recommendations (100 percent coverage). The response time was kept short, with an average of less than 145 milliseconds. The system is lightweight, inexpensive, and simple to install. It is a sensible match to small and middle-sized schools.

Keywords

Online Learning; Personal Suggestions; Finding Similar Users; New User Problem; Mixed Suggestion Method; Spring Boot

References

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