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Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis

The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Qualit...

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Autores principales: Wang, Changlin, Zheng, Puyang, Zhang, Fengrui, Qian, Yufeng, Zhang, Yiyao, Zou, Yulin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802833/
https://www.ncbi.nlm.nih.gov/pubmed/35111102
http://dx.doi.org/10.3389/fpsyg.2021.767310
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author Wang, Changlin
Zheng, Puyang
Zhang, Fengrui
Qian, Yufeng
Zhang, Yiyao
Zou, Yulin
author_facet Wang, Changlin
Zheng, Puyang
Zhang, Fengrui
Qian, Yufeng
Zhang, Yiyao
Zou, Yulin
author_sort Wang, Changlin
collection PubMed
description The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality (EEQ) is essential. In particular, fault tree analysis (FTA), a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning (DL) method to analyze the factors of IEE quality; consequently, based on the traditional FTA, fuzzy fault tree analysis (FFTA) is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey (QS), based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students’ cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of “teaching failure” in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality.
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spelling pubmed-88028332022-02-01 Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis Wang, Changlin Zheng, Puyang Zhang, Fengrui Qian, Yufeng Zhang, Yiyao Zou, Yulin Front Psychol Psychology The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality (EEQ) is essential. In particular, fault tree analysis (FTA), a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning (DL) method to analyze the factors of IEE quality; consequently, based on the traditional FTA, fuzzy fault tree analysis (FFTA) is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey (QS), based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students’ cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of “teaching failure” in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality. Frontiers Media S.A. 2022-01-17 /pmc/articles/PMC8802833/ /pubmed/35111102 http://dx.doi.org/10.3389/fpsyg.2021.767310 Text en Copyright © 2022 Wang, Zheng, Zhang, Qian, Zhang and Zou. 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
Wang, Changlin
Zheng, Puyang
Zhang, Fengrui
Qian, Yufeng
Zhang, Yiyao
Zou, Yulin
Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title_full Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title_fullStr Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title_full_unstemmed Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title_short Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis
title_sort exploring quality evaluation of innovation and entrepreneurship education in higher institutions using deep learning approach and fuzzy fault tree analysis
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802833/
https://www.ncbi.nlm.nih.gov/pubmed/35111102
http://dx.doi.org/10.3389/fpsyg.2021.767310
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