Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology
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 coll...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0611 |
| _version_ | 1797279091152388096 |
|---|---|
| author | Lou Yunjia Xiao Huayi |
| author_facet | Lou Yunjia Xiao Huayi |
| author_sort | Lou Yunjia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T16:20:16Z |
| format | Article |
| id | doaj.art-ef8e5419427b4a77a1705897462efde6 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:20:16Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-ef8e5419427b4a77a1705897462efde62024-03-04T07:30:43ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0611Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data TechnologyLou Yunjia0Xiao Huayi11International College of Krirk University, Bankok, 10220, Thailand.1International College of Krirk University, Bankok, 10220, Thailand.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.https://doi.org/10.2478/amns-2024-0611support vector machinedbt-svm evaluationclassifier combinationkernel function“dual-teacher” teachers62p30 |
| spellingShingle | Lou Yunjia Xiao Huayi Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology Applied Mathematics and Nonlinear Sciences support vector machine dbt-svm evaluation classifier combination kernel function “dual-teacher” teachers 62p30 |
| title | Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology |
| title_full | Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology |
| title_fullStr | Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology |
| title_full_unstemmed | Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology |
| title_short | Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology |
| title_sort | research on the quality assessment model of dual teacher teachers in higher vocational colleges and universities based on big data technology |
| topic | support vector machine dbt-svm evaluation classifier combination kernel function “dual-teacher” teachers 62p30 |
| url | https://doi.org/10.2478/amns-2024-0611 |
| work_keys_str_mv | AT louyunjia researchonthequalityassessmentmodelofdualteacherteachersinhighervocationalcollegesanduniversitiesbasedonbigdatatechnology AT xiaohuayi researchonthequalityassessmentmodelofdualteacherteachersinhighervocationalcollegesanduniversitiesbasedonbigdatatechnology |