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Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education
High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral edu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811253/ https://www.ncbi.nlm.nih.gov/pubmed/35126230 http://dx.doi.org/10.3389/fpsyg.2021.746558 |
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author | Chen, Bailin Liu, Yi Zheng, Jinqiu |
author_facet | Chen, Bailin Liu, Yi Zheng, Jinqiu |
author_sort | Chen, Bailin |
collection | PubMed |
description | High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents with both political integrity and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. |
format | Online Article Text |
id | pubmed-8811253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88112532022-02-04 Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education Chen, Bailin Liu, Yi Zheng, Jinqiu Front Psychol Psychology High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents with both political integrity and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. Frontiers Media S.A. 2022-01-20 /pmc/articles/PMC8811253/ /pubmed/35126230 http://dx.doi.org/10.3389/fpsyg.2021.746558 Text en Copyright © 2022 Chen, Liu and Zheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Chen, Bailin Liu, Yi Zheng, Jinqiu Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title | Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title_full | Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title_fullStr | Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title_full_unstemmed | Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title_short | Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education |
title_sort | using data mining approach for student satisfaction with teaching quality in high vocation education |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811253/ https://www.ncbi.nlm.nih.gov/pubmed/35126230 http://dx.doi.org/10.3389/fpsyg.2021.746558 |
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