On the Study of Small Class Sizes of Primary School Using Machine Learning
- DOI
- 10.2991/978-94-6463-034-3_118How to use a DOI?
- Keywords
- small class size; primary school; data visualization; cluster; K- NearestNeighbor(KNN) prediction model
- Abstract
The purpose of this study is to explore the phenomenon of small classes size in elementary school education in recent years and the differences in the implementation of small-class size education policies in different regions in China. This essay also talked about the motivation, background information and advantages of small class size in Primary Education. Besides, the study use data visualization to analyze the number of primary schools and the number of teachers in 34 provinces in China from 1978 to 2020. Furthermore, the exploration is also focused on the analysis of full-time teachers, enrolments, and their relationship in 34 provinces in China. The last but not the least, this study uses machine learning and other skills to make a prediction model, such as K- NearestNeighbor(KNN). In conclusion, the government should take measures to narrow the gap of educational resources in China.
- 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 - Tian Litong PY - 2022 DA - 2022/12/23 TI - On the Study of Small Class Sizes of Primary School Using Machine Learning BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 1148 EP - 1157 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_118 DO - 10.2991/978-94-6463-034-3_118 ID - Litong2022 ER -