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Entrepreneurship education is emerging around the world. Western developed countries, such as the United States, continue to ensure the quality of innovation and entrepreneurship education and use educational assessments to prevent declines in the quality of relevant talent training (Bauman & Lucy, 2021). However, innovation and entrepreneurship education in China has been plagued by problems such as a lack of funds and a lack of ability to succeed in the growth process. Since the middle and late 20th century, with the important issue of innovative talent training proposed by the “Qian problem,” and after a period of review and standardization, China’s innovation and entrepreneurship education has entered a new stage of systematization and standardization (Sun & Wu, 2016).
As an important base for cultivating innovative talents in the new era, innovation and entrepreneurship education in colleges and universities is directly related to the development of a nation’s innovation ability and entrepreneurial ecology (Xu, 2021). However, the traditional evaluation methods of innovation and entrepreneurship education often rely on pencil-and-paper questionnaires, regular expert reviews, or simple statistics. These methods are not only inefficient but also easily influenced by subjective factors, and it is difficult to comprehensively and objectively reflect the real situation of education quality.
Most of the comprehensive evaluation problems in nature are fuzzy, so fuzzy mathematics with a fuzzy phenomenon as the research object has become a powerful tool to solve such problems, thus forming the fuzzy comprehensive evaluation method. Teaching quality evaluation has a basic feature, that is, fuzziness. For example, satisfaction itself is a concept that is relatively vague, unclear, and difficult to quantify. At present, the method of evaluating teachers’ classroom teaching that has been adopted by many schools is the student scoring method. However, in this method nonquantitative and ambiguous things are forcibly quantified, leading to many problems. To overcome these problems, and improve the evaluation results, the two most commonly used system evaluation methods at present include (a) the fuzzy comprehensive evaluation method and (b) the hierarchical analysis method (Qin et al., 2021).
According to the membership degree theory of fuzzy mathematics, qualitative evaluation is transformed into quantitative evaluation; that is, fuzzy mathematics is used to make an overall evaluation of things or objects that is restricted by many factors. This evaluation method yields clear and systematic results. Compared with the traditional methods, the evaluation content is more comprehensive, the evaluation results are more accurate, and the evaluation organization is more efficient. Fuzzy evaluation combines qualitative evaluation with quantitative evaluation, which can help establish scientific and reasonable assessment indicators and solve vague and difficult-to-quantify problems and is suitable for solving various uncertain problems (Wu, 2014). Fuzzy comprehensive evaluation is usually divided into a target layer and an index layer. Through the fuzzy relation matrix (i.e., membership matrix) that lies between the indicator layer and the evaluation set, the membership vector of the target layer to the evaluation set can be obtained, as can the comprehensive evaluation result of the target layer.
The fuzzy evaluation method does have some limitations. The construction of the fuzzy evaluation model needs to comprehensively consider many factors, including teaching content, teaching staff, teaching resources, student satisfaction, and so on. However, because of the dissimilar orientation and characteristics of innovation and entrepreneurship education in different universities, the universality of the evaluation model has, to some extent, been limited. Therefore, in practical application it is necessary to adjust and optimize the model according to the characteristics and positioning of different universities, so as to improve the model’s universality and operability. At the same time, with the development and deepening of innovation and entrepreneurship education and practice, the model should be regularly updated and optimized to meet new needs and challenges.