Research and Analysis of Student Behavior Portrait Based on Big Data
- DOI
- 10.2991/978-94-6463-192-0_51How to use a DOI?
- Keywords
- Big Data; Portrait of student behavior; Research and analysis
- Abstract
The application of big data in the Internet, cloud computing and other fields is becoming more and more extensive, with the continuous increase of the network environment and information resources of students’ campuses, the network structure of university libraries has also undergone tremendous changes, and the amount of data storage has also become larger and larger. How to use massive data mining technology to effectively classify and analyze students’ campus information has become an urgent problem to be solved. In this paper, we mainly study the modeling and profiling of college students’ behavior characteristics based on big data analysis and predictive training set. Firstly, this paper studies the theoretical techniques related to big data, including data mining algorithms and behavioral profiling overviews. Secondly, the analysis of student behavior portraits was carried out; Finally, the fusion verification of the student behavior portrait model was carried out, and the experimental results were analyzed and summarized.
- 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 - Jiameng Zhang PY - 2023 DA - 2023/07/04 TI - Research and Analysis of Student Behavior Portrait Based on Big Data BT - Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023) PB - Atlantis Press SP - 393 EP - 401 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-192-0_51 DO - 10.2991/978-94-6463-192-0_51 ID - Zhang2023 ER -