Logistic Empirical Study of Regression Model in Python Class Model Examination
College of Digital Technology Application Industry, Shangrao Normal University, Shangrao 334001, China
| Abstract: | In the information age, data holds increasingly significant value, and computer-based data analysis and visualization have become widespread practices. This paper explores the application of Logistic regression models in analyzing factors influencing student performance. Logistic regression is a supervised learning algorithm that predicts a binary dependent variable based on a set of independent variables, transforming the predicted value of linear regression into a probability through a sigmoid function. In the context of education, student achievement is often categorized, making Logistic regression well-suited for analyzing its influencing factors. By collecting multifaceted data from students, the Logistic regression model can establish a predictive model with multiple independent variables, such as personal characteristics, family factors, and school environment. This study focuses on the analysis of Python course model examination results, preprocessing the data to ensure integrity and addressing abnormal values. Visualization analysis is employed to intuitively reveal the potential effects of various factors, including gender, average daily learning time, undergraduate school level, and registered school level, on model exam scores. The results demonstrate significant correlations between these factors and exam scores, with Logistic regression analysis further explaining about 72% of data performance variation. This study provides insights into using data visualization and Logistic regression for educational research and decision-making. |
| Keywords: | Visualization; Python; Logistic Regression Model; Matplotlib |
| DOI: | 10.57237/j.jeit.2025.01.001 |
View:
Downloads:
Received: 17 December 2024
Accepted: 17 January 2025
Published Online: 8 February 2025
References
| [1] | Song Yongsheng, Huang Rongmei. Academic performance analysis and visual analysis based on Python [J]. Information Technology and Information Technology, 2021, (12): 100-102. |
| [2] | Xiaoling Shi, Gong Yang. Python-based student achievement analysis and visual presentation study [J]. Journal of Hebei Software Vocational and Technical College, 2022, 24(02): 4-6. https://doi.org/10.13314/j.cnki.jhbsi.2022.02.013 |
| [3] | Kirthi Raman. Python Data Visualization [M]. Machinery Press, 2017. |
| [4] | Li Guo, Zhang Meng, Kang Rui. Research on student performance prediction model based on logistic regression [J]. China Information Technology Education, 2023. |
| [5] | Katsiaryna K, T S H, Chang Woo J, et al. Benefits of implementing student-led revi ew and mock examinations in the medical undergraduate gross anatomycurriculum. [J]. Medicalteacher, 2022, 44(9): 1-4. |
| [6] | Cai Zhenhai. Explore the practice of abnormal data processing and analysis based on Python [J]. Computer Knowledge and Technology, 2023, 19(27): 62-65. https://doi.org/10.14004/j.cnki.ckt.2023.1434 |
| [7] | Yu Feiyang, Yang Hengjie. Design and implementation of the data analysis software based on Python [J]. Modern Computer, 2023, 29 (12): 99-103. |
| [8] | Bao Peidong, Wan Nan, Wang Tingting, etc.. Research on data climbing and data visualization analysis of new energy vehicles based on Python [J]. Light Industry Technology, 2023, 39(05): 105-107. |
| [9] | Wang, L., & Zhang, J. (2023). Factors Influencing Academic Performance: An Analysis Based on Logistic Regression Model. Journal of Educational Research, 42(3), 123-135. |
| [10] | Zhao, M., Liu, X., & Chen, H. (2022). Gender Differences in Academic Performance: Evidence from a Large-Scale Dataset. Gender and Education, 34(1), 56-72. |
| [11] | Johnson, A., & Smith, B. (2021). The Impact of Undergraduate Institution on Graduate Performance: A Comparative Analysis. Higher Education Quarterly, 45(2), 110-128. |
| [12] | Chen, Y., & Wang, Z. (2023). Data Visualization Techniques for Educational Research: A Review. Journal of Educational Data Mining, 15(1), 45-60. |
| [13] | Taylor, K., & Davis, L. (2022). Logistic Regression Modeling in Educational Research: A Practical Guide. Journal of Educational Measurement, 55(4), 301-319. |
| [14] | Liu, W., & Zhang, H. (2021). The Role of Study Time in Academic Performance: Insights from a Longitudinal Study. Journal of Learning Analytics, 8(2), 147-163. |
| [15] | Brown, J., & Lee, H. (2023). The Effects of Online Learning on Student Engagement and Academic Performance. Journal of Distance Education, 38(1), 23-45. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us