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

<p>The cultivation path of the applied “dual-teacher” teacher team in this paper mainly relies on the qualification standard and evaluation system. Big data technology is used to collect data, deeply mine fragmented data, and further extract index factors that affect the quality of teachers. The multi-level fuzzy hierarchical evaluation model realizes the quantification of the decision-making process with less quantitative information, and determines the weights through the multi-level comparison of importance, to test and evaluate the teaching ability of “dual-teacher” teachers. The results show that, among the 166 survey respondents, 77 respondents belong to the “ Dual-teacher” teachers, accounting for about 45.03%. The primary needs and ways for most of the teachers to participate in the training of “dual-teacher” teachers are to improve the practical ability of industry and enterprise positions (score 0.86) and the school-based training of dual-teacher teachers (0.93), which is centralized in the college. In evaluating “dual-teacher” teaching, the weight of industry-teaching integration is 0.3378, so the ability of industry-teaching integration is a more meaningful indicator. College students are satisfied with “dual-teacher teaching”, and the score shows that the essential satisfaction is more than half of the total number of samples, which is 343.</p>
</abstract>

The cultivation path of the applied “dual-teacher” teacher team in this paper mainly relies on the qualification standard and evaluation system. Big data technology is used to collect data, deeply mine fragmented data, and further extract index factors that affect the quality of teachers. The multi-level fuzzy hierarchical evaluation model realizes the quantification of the decision-making process with less quantitative information, and determines the weights through the multi-level comparison of importance, to test and evaluate the teaching ability of “dual-teacher” teachers. The results show that, among the 166 survey respondents, 77 respondents belong to the “ Dual-teacher” teachers, accounting for about 45.03%. The primary needs and ways for most of the teachers to participate in the training of “dual-teacher” teachers are to improve the practical ability of industry and enterprise positions (score 0.86) and the school-based training of dual-teacher teachers (0.93), which is centralized in the college. In evaluating “dual-teacher” teaching, the weight of industry-teaching integration is 0.3378, so the ability of industry-teaching integration is a more meaningful indicator. College students are satisfied with “dual-teacher teaching”, and the score shows that the essential satisfaction is more than half of the total number of samples, which is 343.

The Path of “Dual-Teacher” Teacher Cultivation in Applied Local Higher Teacher Colleges under the Background of Big Data

Applied Mathematics and Nonlinear Sciences

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

The cultivation path of the applied “dual-teacher” teacher team in this paper mainly relies on the qualification standard and evaluation system. Big data...

The Path of “Dual-Teacher” Teacher Cultivation in Applied Local Higher Teacher Colleges under the Background of Big Data

Published Online: May 03, 2024

Page range: -

Received: Apr 09, 2024

Accepted: Apr 26, 2024

DOI: https://doi.org/10.2478/amns-2024-0972

Keywords
Deep mining, Multi-layer fuzzy, Evaluation system, Dual-teaching

© 2024 Qiuyue Zhang et al., published by Sciendo

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

The Path of “Dual-Teacher” Teacher Cultivation in Applied Local Higher Teacher Colleges under the Background of Big Data

Published Online: May 03, 2024

Page range: -

Received: Apr 09, 2024

Accepted: Apr 26, 2024

DOI: https://doi.org/10.2478/amns-2024-0972

KeywordsDeep mining, Multi-layer fuzzy, Evaluation system, Dual-teaching

© 2024 Qiuyue Zhang et al., published by Sciendo

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

Keywords
Deep mining, Multi-layer fuzzy, Evaluation system, Dual-teaching