<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To further analyze the content of “dual-teacher” teacher team construction in higher vocational colleges and universities, this paper focuses on teacher moral construction, teacher training, assessment and evaluation, and incentives for in-depth investigation. It is clear that higher vocational colleges and universities should distinguish between “dual-teacher” teacher quality assessment and ordinary teacher assessment, and put forward the gradient “dual-teacher” teacher quality team construction. Improve the support vector machine, use the binary tree to propose an evaluation model based on incomplete DBT-SVM, and combine multiple binary classifiers to solve the multi-classification problem of “dual-teacher” teacher quality evaluation. The optimal parameter combinations, i.e., <italic>C</italic> =2<sup>8</sup> and <italic>γ</italic> = 0.0534, are obtained using the kernel function and parameter tuning experiments, and the accuracy of the model prediction results reaches 94.325%, taking the “dual-teacher” teachers in a university in Y province as the specific evaluation object. This shows that the accuracy of this DBT-SVM-based evaluation method of “dual-teacher” teacher quality is good.</p>
</abstract>

To further analyze the content of “dual-teacher” teacher team construction in higher vocational colleges and universities, this paper focuses on teacher moral construction, teacher training, assessment and evaluation, and incentives for in-depth investigation. It is clear that higher vocational colleges and universities should distinguish between “dual-teacher” teacher quality assessment and ordinary teacher assessment, and put forward the gradient “dual-teacher” teacher quality team construction. Improve the support vector machine, use the binary tree to propose an evaluation model based on incomplete DBT-SVM, and combine multiple binary classifiers to solve the multi-classification problem of “dual-teacher” teacher quality evaluation. The optimal parameter combinations, i.e., C =28 and γ = 0.0534, are obtained using the kernel function and parameter tuning experiments, and the accuracy of the model prediction results reaches 94.325%, taking the “dual-teacher” teachers in a university in Y province as the specific evaluation object. This shows that the accuracy of this DBT-SVM-based evaluation method of “dual-teacher” teacher quality is good.

Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology

International College of Krirk University

Applied Mathematics and Nonlinear Sciences

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

To further analyze the content of “dual-teacher” teacher team construction in higher vocational colleges and universities, this paper focuses on teacher moral...

Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 20. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0611

Schlüsselwörter
Support vector machine, DBT-SVM evaluation, Classifier combination, Kernel function, “Dual-teacher” teachers

© 2023 Yunjia Lou et al., published by Sciendo

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

Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 20. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0611

SchlüsselwörterSupport vector machine, DBT-SVM evaluation, Classifier combination, Kernel function, “Dual-teacher” teachers

© 2023 Yunjia Lou et al., published by Sciendo

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

Schlüsselwörter
Support vector machine, DBT-SVM evaluation, Classifier combination, Kernel function, “Dual-teacher” teachers