<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Along with the development of Chinese journalism and the continuous exploration of journalistic genres in the journalism industry, non-fiction writing has been shuttling between the two genres of literature and journalism. The development background of the times traditional non-fiction writing course teaching mode is difficult to adapt to the needs of college students, this paper around the decentralized Internet-based model of non-fiction writing in local college writing course application research, firstly, to build a non-fiction writing platform mathematical model, Markov random field, through the Bayesian network algorithm to process the characteristics of college students in the classroom data, the results show that students within the platform The results show that as the number of tests increases, the score trend from the three aspects of writing ideas and writing style and writing skills all show an upward trend, and the average score basically stays between 80-90, which is beneficial to understand the students’ writing level and performance, provide different users with suitable learning content for themselves, and contribute to the cause of Chinese literature by improving the students’ ability of non-fiction writing. It is of great value in changing the closed mode of writing courses, enhancing the practicality of writing content, and training college students to think logically and critically.</p>
</abstract>

Along with the development of Chinese journalism and the continuous exploration of journalistic genres in the journalism industry, non-fiction writing has been shuttling between the two genres of literature and journalism. The development background of the times traditional non-fiction writing course teaching mode is difficult to adapt to the needs of college students, this paper around the decentralized Internet-based model of non-fiction writing in local college writing course application research, firstly, to build a non-fiction writing platform mathematical model, Markov random field, through the Bayesian network algorithm to process the characteristics of college students in the classroom data, the results show that students within the platform The results show that as the number of tests increases, the score trend from the three aspects of writing ideas and writing style and writing skills all show an upward trend, and the average score basically stays between 80-90, which is beneficial to understand the students’ writing level and performance, provide different users with suitable learning content for themselves, and contribute to the cause of Chinese literature by improving the students’ ability of non-fiction writing. It is of great value in changing the closed mode of writing courses, enhancing the practicality of writing content, and training college students to think logically and critically.

A study on the application of non-fiction writing in writing courses in local universities based on a decentralized Internet model

Applied Mathematics and Nonlinear Sciences

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

Along with the development of Chinese journalism and the continuous exploration of journalistic genres in the journalism industry, non-fiction writing has been...

A study on the application of non-fiction writing in writing courses in local universities based on a decentralized Internet model

Online veröffentlicht: 21. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Nov. 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00224

Schlüsselwörter
Decentralised Internet model, Non-fiction writing, Mathematical models of platforms, Bayesian network algorithms, Markov random fields.

© 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 non-fiction writing in writing courses in local universities based on a decentralized Internet model

Online veröffentlicht: 21. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Nov. 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00224

SchlüsselwörterDecentralised Internet model, Non-fiction writing, Mathematical models of platforms, Bayesian network algorithms, Markov random fields.

© 2023 Qingbo Zhang, published by Sciendo

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

Schlüsselwörter
Decentralised Internet model, Non-fiction writing, Mathematical models of platforms, Bayesian network algorithms, Markov random fields.