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Construction of Student Innovation and Entrepreneurship Experience System Integrating K-Means Clustering Algorithm

Author

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  • Fanna Zou
  • Rui Li
  • Sagheer Abbas

Abstract

The traditional talent development model is unlikely to satisfy the demands of the times and socioeconomic growth. Innovation-driven development policies require a great deal of creativity and entrepreneurship. Student creativity and education in the enterprise have emerged as an essential source of concern for all segments of society. The traditional talent cultivation mode has been unable to meet the requirements of the economic growth of the society. College graduates are national talents of high quality and high level, which are important talent resources for colleges and universities to grow sustainably. Innovation and entrepreneurship education for college students is a common concern of the current society. The research and implementation of innovation and entrepreneurship education for Chinese college students are progressing rapidly, but the innovation and entrepreneurship education of Chinese college students began later and is not at a high level of development at present. Promoting student innovation and entrepreneurship requires further improvements in research and teaching methods. This article presents the scientific classification and the K-means algorithm. It defines the concepts related to the education system of change and entrepreneurship, the concepts related to the search for change and the theories of entrepreneurship, and the theoretical foundations of the development of the education system. Of the researchers, 39.47% were male; 60.53% were female; 13.68% were students; 70.53% were undergraduate students; and 15.79% were postgraduate students.

Suggested Citation

  • Fanna Zou & Rui Li & Sagheer Abbas, 2022. "Construction of Student Innovation and Entrepreneurship Experience System Integrating K-Means Clustering Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, July.
  • Handle: RePEc:hin:jnlmpe:1325873
    DOI: 10.1155/2022/1325873
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