Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research and Analysis Based on Cloud Class Data

Download as PDF

DOI: 10.23977/aetp.2023.071508 | Downloads: 8 | Views: 261

Author(s)

Hengrui Pan 1, Zixia You 1, Xinhong Liu 1, Piao Yang 1, Yiming Liu 1, Sirui Ding 1

Affiliation(s)

1 Beijing Institute of Petrochemical Technology, Beijing, 102617, China

Corresponding Author

Xinhong Liu

ABSTRACT

This article applies the theoretical methods of probability and statistics, using probability and statistics cloud class courses as carriers, to study and analyze a large amount of data generated in cloud class courses, establish a mathematical model of related variables, and predict the final grades of students in cloud class courses. By presenting a learning situation data model, students can timely see their own problems and recognize the importance of process learning. At the same time, it provides a basis for teachers to conduct teaching behavior analysis and provide personalized and precise learning guidance for students.

KEYWORDS

Cloud class, correlation analysis, regression analysis

CITE THIS PAPER

Hengrui Pan, Zixia You, Xinhong Liu, Piao Yang, Yiming Liu, Sirui Ding, Research and Analysis Based on Cloud Class Data. Advances in Educational Technology and Psychology (2023) Vol. 7: 69-73. DOI: http://dx.doi.org/10.23977/aetp.2023.071508.

REFERENCES

[1] Sheng Ju, Xie Shiqian, et al. Probability theory and mathematical statistics [M]. Beijing, higher education press, 2009
[2] Wang Jiale, Wang Shizhi, Huang Qian. Prediction of coal mine water inflow based on correlation analysis [J]. Shaanxi Coal, 2021, 40 (02): 99-102
[3] Chen Wei, Pan Liying, Lin Chaoran. Research on the Competitiveness of Knowledge Intensive Manufacturing Industry Based on Canonical Correlation Analysis [J]. Learning and Exploration, 2021 (03): 113-119
[4] Zhang Houcan, Xu Jianping. Modern Psychology and Educational Statistics. 3rd Edition [M]. Beijing Normal University Press, 2009
[5] Li Qiuyao. Application of Multiple Linear Regression Model in River Water Quality Prediction [J]. Information Systems Engineering, 2023 (07): 79-82
[6] Yuan Qi. Empirical Study on the Factors Influencing China's Foreign Exchange Reserves Based on Multiple Linear Regression Models [J]. China Business Review, 2023 (13): 8-11
[7] Ying Xiyuan, Sa Binhan. Prediction of Strawberry Volume and Quality Based on Linear Regression Models [J]. Advanced Mathematics Research, 2023, 26 (03): 86-90
[8] Miao Hui, Luo Lulu. Prediction of Logistics Demand in Guizhou Province Based on Multiple Linear Regression Model [J]. Logistics Technology, 2023, 46 (08): 75-78
[9] Yang Xinchun, You Wei, Wan Xiangyu, et al. Reconstruction of Land Water Storage Changes in Nine Major Watersheds in China Using Multiple Linear Regression Models [J]. Geodesy and Geodynamics, 2023, 43 (02): 116-120
[10] Wan Jianguo. Application of Multiple Linear Regression Model in Owner Satisfaction Evaluation: A Case Study of Residential Owners in Nanning City [J]. Real Estate World, 2023 (03): 103-105

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.