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Regression analysis of intelligent education based on linear mixed effect model

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Abstract

In order to explore the regression effect of the intelligent education model, based on the linear mixed effect model, this paper constructs a regression analysis model based on the linear mixed effect model and studies the estimation algorithm of the linear mixed effect model under massive data. Moreover, this paper combines the three-step estimation method with the divide-and-conquer algorithm to propose a three-stage estimation algorithm. In addition, this paper studies the estimation algorithm of the semi-parametric mixed effect model under massive data, and combines the local linear least square method with the divide and conquer algorithm to obtain a new estimation algorithm. At the same time, this article extends the estimation algorithm to the semi-parametric mixed effect model under massive data to obtain a new estimation algorithm, so that the semi-parametric mixed model is also applicable under massive data. Finally, this paper constructs an intelligent education regression analysis model with data mining function based on actual needs, and designs experiments to verify the performance of the model. The research results show that the model constructed in this paper has a certain effect.

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Acknowledgements

This paper was supported by (1) Research Results of Zhejiang Federation of Social Sciences: Research and Effectiveness of Boppps Model Embedded in Accounting Wisdom Classroom in Post Epidemic Era(2021N102); (2) “The 13th Five-Year Plan” Teaching Reform Research Project of Zhejiang Higher Education:Research on The Construction of Capital Operation Case Set Based on “Wenzhou Spirit” (jg20190563).

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  1. School of Management, Wenzhou Business College, Wenzhou, 325035, China

    Haiyan Zhou, Sai Jiang & Xiaojing Liu

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  1. Haiyan Zhou
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  2. Sai Jiang
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  3. Xiaojing Liu
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Correspondence to Sai Jiang.

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Zhou, H., Jiang, S. & Liu, X. Regression analysis of intelligent education based on linear mixed effect model. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03038-7

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