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With the development of globalization, transnational higher education is gradually becoming an essential means for many countries to achieve internationalization. Nowadays, Sino-foreign cooperative universities and programmeshave been turned into an important form of internationalization of Chinese higher education, increasingly attracting the attention among policy-makers and academic researchers. This paper focuses on student satisfaction with Sino-foreign cooperative universities by both quantitative and qualitative approaches. Based on the case study, the development trend of Sino-foreign cooperative universities is discussed. Therefore, this paper is of far-reaching significance in improving the student satisfaction of Sino-foreign cooperative universities and promoting the internationalization of other ordinary public universities in China. Through the mixed empirical methods and case study approach, this paper found that the student evaluation of Sino-foreign cooperative universities shows the characteristics of "integration" that effectively absorbs the maternal resources and characteristics of both Chinese and foreign parties, and also realizes self-management and education innovation. [ABSTRACT FROM AUTHOR]

As we all know, the random forest algorithm has the advantages of high classification intensity and wide application range. Nevertheless, it still has a lot of room for improvement. This paper introduces the basic idea and working principle of classification algorithm and random forest algorithm, so this paper proposes some improved algorithms on the basis of all current research studies. College students, as the most innovative and adaptable group, have gradually become a development direction of China's education reform to cultivate their innovative employment ability and develop innovative employment education. By constructing a model of the relationship between innovation and entrepreneurship education and college students' employability, this paper examines the impact of innovation and entrepreneurship education mode on college students' employment and entrepreneurship. At the same time, the model is tested by improving the random forest algorithm. The process of talent quality evaluation is to select talents with the best comprehensive quality based on various indicators of students' performance in school, which can be regarded as a classification problem of unbalanced data sets. The improved random forest algorithm proposed in this paper has little difference in precision and recall rate when it is used for talent training evaluation but has a certain degree of improvement in accuracy rate, which meets the design requirements. [ABSTRACT FROM AUTHOR]

The global economic trends and the winds of technological change have elevated the status of integration between industry and education for innovation and entrepreneurship. Technologies and the Internet have significantly changed all aspects of our lives. College education has improved as a result of the enormous benefits of the Internet and big data. Reforms and development have still a lot of application space in the training and practice of educational technology talents of a college education. To improve the performance evaluation capabilities of educational technology experts in Chinese universities through education and "Internet +" has become a challenge that has to be addressed. This article is under such a formal background to have a detailed study about the current college education in China. In this study, an analysis is presented for the performance evaluation of the education technology talent training in modern universities based on big data technology. A fuzzy evaluation algorithm based on composite elements is proposed to evaluate the multielement and multilevel college education system. Experimental results illustrate the feasibility and effectiveness of the proposed algorithm. The proposed method has the potential to solve the complicated performance evaluation problem in colleges. [ABSTRACT FROM AUTHOR]