Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

<Previous Article In Volume
Next Article In Volume>

Analyzing Student Achievement: Data Processing Models in Education

Authors
Lei Wang1, *
1Ningbo Foreign Language School, Ningbo, China
*Corresponding author. Email: rain_wanglei@outlook.com
Corresponding Author
Lei Wang
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_66How to use a DOI?
Keywords
Analyzing; Predicting; Chart; Pandas
Abstract

Multiple factors contribute in a non-linear manner, making the field more attractive. The wide availability of educational datasets further fuels this interest. The potential application of data processing models in the field of education is: Data analytics help teachers to understand the learning abilities and challenges of their students and promote a deeply ingrained cultural process of using detailed inputs (information) to ensure optimal outputs (student outcomes). The purpose of this article is in this literature it will show the prediction of student achievement. First of all, it is necessary to analyze the data so this requires some Python models like Pandas, also some charts like pie charts, bar charts and so on. With the increasing size of schools in our society, the problem of ensuring and improving the quality of teaching is becoming more and more prominent, and various teaching research and teaching practices are emerging. Evaluation is used to improve the quality of teaching and to motivate students to study hard.

Copyright
© 2024 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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_66
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_66How to use a DOI?
Copyright
© 2024 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

risenwbib
TY  - CONF
AU  - Lei Wang
PY  - 2024
DA  - 2024/02/14
TI  - Analyzing Student Achievement: Data Processing Models in Education
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 646
EP  - 655
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_66
DO  - 10.2991/978-94-6463-370-2_66
ID  - Wang2024
ER  -