Abnormal Student Detection Model Based on Student Feature Extraction

Integrated Learning Based on Clustering and Neural Network

DOI

10.2991/978-94-6463-034-3_98How to use a DOI?

Keywords

Abnormal students; Semantic Library; K-Means Clustering; Neural Network; Integrated Learning

Abstract

Moral education is an important mission of colleges and universities, and the identification and tracking of abnormal students is an important part of moral education. However, problems such as large amount of data related to college students and the majority of text information are prominent. In this paper, the semantic library is used to quantify the text information, and the emotional feature vector of students is obtained. Combined with the digital data, the feature vector of students is effectively obtained. The K-Means clustering and neural network ensemble learning are used to realize the identification of characteristic students, and the accuracy rate can reach 82.02%, which has certain reference significance.

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.

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TY  - CONF
AU  - Shuo Zhang
AU  - Xiangchao Wen
PY  - 2022
DA  - 2022/12/23
TI  - Abnormal Student Detection Model Based on Student Feature Extraction
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 957
EP  - 965
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_98
DO  - 10.2991/978-94-6463-034-3_98
ID  - Zhang2022
ER  -