Application of Data Mining Technology in Personalized Curriculum Recommendation of Vocational Education Learning Platform
- DOI
- 10.2991/978-94-6463-222-4_42How to use a DOI?
- Keywords
- data mining; Learning; Personalized curriculum
- Abstract
In order to solve the problem of personalized learning, a kind of data mining technology is proposed as a recommended application in the personalized curriculum of the vocational education learning platform. This paper uses data mining technology and learning analysis technology to build relevant models in the field of education, explore the relationship between education variables, and apply the improved association rule mining algorithm to the analysis of the relationship between course settings and course grades in the network platform, find meaningful information between courses, and then analyze the valuable knowledge, Find out the gains and losses of various aspects of teaching and the internal factors that affect students’ performance, and then provide decision support for students’ course selection, teachers’ teaching and teaching management.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zhongying Yang PY - 2023 DA - 2023/08/28 TI - Application of Data Mining Technology in Personalized Curriculum Recommendation of Vocational Education Learning Platform BT - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023) PB - Atlantis Press SP - 390 EP - 396 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-222-4_42 DO - 10.2991/978-94-6463-222-4_42 ID - Yang2023 ER -