<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Under the background of the big data era, the rapid development of the Internet has driven the development of the education field. The traditional creative writing teaching is hardly suitable for the new curriculum reform requirements, prompting the rapid rise of creative writing and being highly recognized by the National Education Bureau. This paper applies creative writing based on multiple linear regression algorithms, i.e., teaching literature and education. Firstly, we understand the formula and principle of multiple linear regression algorithm, establish multiple linear models, extract and matrix students’ characteristic data, collect students’ data when learning on the online platform, pre-process the collected data, and analyze the performance and behavior of creative writing based on the end element linear regression algorithm. The results show that the number of clustering centers and visits are linearly correlated with the student’s grades, final overall grades, and test scores and that the student’s grades eventually improved to above 90. This study identifies the extent to which learning behaviors impact students’ final grades, allowing teachers to refine their teaching methods by selecting the learning styles that have a greater impact on students’ final grades among the many online platform student activities. It is conducive to improving cultural literacy and creative writing skills and has historical significance for developing Chinese literature and education.</p>
</abstract>

Under the background of the big data era, the rapid development of the Internet has driven the development of the education field. The traditional creative writing teaching is hardly suitable for the new curriculum reform requirements, prompting the rapid rise of creative writing and being highly recognized by the National Education Bureau. This paper applies creative writing based on multiple linear regression algorithms, i.e., teaching literature and education. Firstly, we understand the formula and principle of multiple linear regression algorithm, establish multiple linear models, extract and matrix students’ characteristic data, collect students’ data when learning on the online platform, pre-process the collected data, and analyze the performance and behavior of creative writing based on the end element linear regression algorithm. The results show that the number of clustering centers and visits are linearly correlated with the student’s grades, final overall grades, and test scores and that the student’s grades eventually improved to above 90. This study identifies the extent to which learning behaviors impact students’ final grades, allowing teachers to refine their teaching methods by selecting the learning styles that have a greater impact on students’ final grades among the many online platform student activities. It is conducive to improving cultural literacy and creative writing skills and has historical significance for developing Chinese literature and education.

A study on the application of creative writing in literature teaching based on multiple linear regression algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the background of the big data era, the rapid development of the Internet has driven the development of the education field. The traditional creative...

A study on the application of creative writing in literature teaching based on multiple linear regression algorithm

Online veröffentlicht: 21. Aug. 2023

Seitenbereich: -

Eingereicht: 01. Nov. 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00227

Schlüsselwörter
Big data, Multiple linear regression, Clustering centers, Feature data matrix, Creative writing.

© 2023 Qingbo Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the application of creative writing in literature teaching based on multiple linear regression algorithm

Online veröffentlicht: 21. Aug. 2023

Seitenbereich: -

Eingereicht: 01. Nov. 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00227

SchlüsselwörterBig data, Multiple linear regression, Clustering centers, Feature data matrix, Creative writing.

© 2023 Qingbo Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data, Multiple linear regression, Clustering centers, Feature data matrix, Creative writing.