Research on practical teaching of innovation and entrepreneurship training program for college students based on big data analysis

Abstract

This article explores how digital technologies such as big data and cloud computing promote college students’ innovation and entrepreneurship, especially the impact of innovation and entrepreneurship training programs on college students’ entrepreneurial intentions. The article adopts big data analysis techniques to screen variables, set research hypotheses, and use partial least squares regression to quantitatively analyze the correlation between university innovativeness and training programs. It was found that the number of university intellectual property rights was significantly associated with the objectives of the training program, with a regression coefficient of 0.069. Further, the article pointed out that most students believed that the regulation of the research segment was the weakest. Therefore, the article suggests improving the training program supervision system, significantly strengthening the supervision of the research session, and also explores the correlation between academic professional factors and faculty guidance.

Keywords

• extensive data analysis
• partial least squares
• precision analysis
• regression analysis
• innovation and entrepreneurship
• 00a73