Prediction of online and offline academic performance of college students based on partial least squares regression analysis

Xiao Chen

doi:10.1117/12.2671893

28 March 2023 Prediction of online and offline academic performance of college students based on partial least squares regression analysis

Xiao Chen

Author Affiliations +

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125970A (2023) https://doi.org/10.1117/12.2671893
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

This paper selects the online and offline learning data of ten students to calculate six component functions of the independent variable group and the dependent variable. The three principal components with large contribution rate was selected to obtain the regression equation between the standardized variable group and the three component variables, the regression equation between the standardized independent variable and the standardized dependent variable, and the regression equation between the original independent variable and the original dependent variable, and finally to obtain the regression prediction value. The MATLAB program was used to generate regression coefficient histogram and regression prediction score chart. The conclusion that the regression equation has a good prediction effect is obtained by using the regression prediction value and the regression prediction diagram. The independent variables that play an important role in explaining the regression equation among the five independent variable factors are obtained from the regression coefficient histogram.

Citation Download Citation

Xiao Chen "Prediction of online and offline academic performance of college students based on partial least squares regression analysis", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125970A (28 March 2023); https://doi.org/10.1117/12.2671893

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KEYWORDS

Online learning

Analytical research

MATLAB

Linear regression

Data modeling

Histograms

Lithium

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