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

Puyang Zheng,Yulin Zou,Fengrui Zhang,Yufeng Qian,Yiyao Zhang,Changlin Wang

doi:10.3389/fpsyg.2021.767310

Abstract

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.

Highlights

With the expanding recruitment of Chinese higher institutions, the undergraduate (UG) number increases year by year, and the pressure of job competition is increasing
The following two ways can be concluded to promote UGs’ deep learning (DL) and improve the efficiency of Innovation and Entrepreneurship Education (IEE) classroom teaching in higher institutions, as shown in Figure 14: Figure 14 reveals that in the process of IEE classroom teaching in higher institutions, learners’ problem-solving ability and evaluation and reflection ability will be significantly affected by learning motivation, but the above two learning paths have not developed learners’ critical and innovative thinking
To improve the quality of IEE classroom teaching for college teachers and students, this study uses the questionnaire survey (QS) method and software engineering method to evaluate the reliability of IEE classrooms for college teachers and students based on DL and fuzzy fault tree analysis (FFTA), respectively; the model accuracy is verified by QS, and the factors of IEE quality are analyzed

Summary

Introduction

With the expanding recruitment of Chinese higher institutions, the undergraduate (UG) number increases year by year, and the pressure of job competition is increasing. In this case, entrepreneurship becomes one of the solutions to the problem for college graduates. The improvement of Innovation and Entrepreneurship Education (IEE) quality in higher institutions can promote UGs’ innovation and entrepreneurship ability and strengthen their entrepreneurial motivation. It is necessary to evaluate the IEE quality in higher institutions to unveil the existing problems of the current teaching modes, based upon which targeted improvement strategies can be put forward to improve the employment rate of UGs (Li et al, 2020)

Results

Discussion

Conclusion