Analysis of Vocational Education Management Data Based on Immune RBF Network Model
- DOI
- 10.2991/978-94-6463-230-9_106How to use a DOI?
- Keywords
- data management; Artificial immunity; RBF network; vocational education
- Abstract
In order to solve the problem that teachers cannot communicate with students “face to face” in the process of online open course teaching, so they cannot intuitively grasp the students’ learning situation. An analysis method of vocational education management data based on immune RBF network model is proposed. In this paper, the immune RBF network is used to establish a student learning data management model. After the online user data is modelled, the model can be used to predict the attention of corresponding students, so as to help teachers develop targeted teaching plans and improve the teaching effect of online courses. Through the verification of actual teaching data, the evaluation system of the data management model proposed in this paper is objective and scientific, and the prediction accuracy rate can reach more than 80%. By using the student data management model proposed in this paper, teachers can carry out teaching according to the characteristics of students, and achieve teaching according to their aptitude in a real sense, which is of great significance to teaching reform and teaching effect.
- 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 - Kun Yang AU - Hongping Zeng PY - 2023 DA - 2023/09/04 TI - Analysis of Vocational Education Management Data Based on Immune RBF Network Model BT - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) PB - Atlantis Press SP - 877 EP - 881 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-230-9_106 DO - 10.2991/978-94-6463-230-9_106 ID - Yang2023 ER -