This study applies the data analysis method of fuzzy clustering to conduct profile analysis and retrospective measurement of STEM university students in project-based learning (PBL) in a special project course. This study analyzes the changes in non-cognitive latent traits of STEM university students after they participate in PBL. The variables used for fuzzy clustering are the changes in non-cognitive latent traits of STEM university students after they participate in PBL. The profile analysis explores the differences in non-cognitive latent traits among the clusters of STEM students. The sample consists of 230 STEM students from a public university in Taiwan. These non-cognitive latent traits include learning satisfaction, grit, mindset (growth mindset/fixed mindset) and self-efficacy. The STEM students come from four departments, namely science (S), technology (T), engineering (E), and mathematics (M). The results of the study indicate that after one semester of PBL in a special project course, the students’ non-cognitive latent traits significantly improve. Students majoring in science and engineering have significantly improvement in learning satisfaction, grit, growth mindset, and self-efficacy, but have slightly declined in fixed mindset, not to the significant level. Students majoring in technology and mathematics have significantly improved their learning satisfaction, grit, growth mindset, and self-efficacy, while their fixed mindset has significantly decreased. For students of different genders, both of them have significant improvement in learning satisfaction, grit, growth mindset, and self-efficacy. On the contrary, fixed mindset has significantly decreased. Based on the changes in non-cognitive latent traits, fuzzy clustering identifies three clusters of STEM students. Additionally, profile analysis reveals that each cluster exhibits unique characteristics and there are significant differences in the changes in their non-cognitive latent trait among clusters. This study provides valuable methodological insights by integrating fuzzy clustering and profile analysis. Moreover, the findings of this study also provide insights and implications for STEM education.

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