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Emotional Mechanisms in Supervisor-Student Relationship:Evidence from Machine Learning and Investigation

Journal of Social Computing 2023年第1期4卷 30-45页

作者： Jingyi Hu Yaxuan Liu Qijian Zheng Feng Liu Faculty of Economics and Management East China Normal UniversityShanghai 200062China Faculty of Education East China Normal UniversityShanghai 200062China School of Computer Science and Technology East China Normal UniversityShanghai 200062China. School of Computer Science East China Normal UniversityShanghai 200062China School of Internet of Things Engineering Wuxi UniversityWuxi 210044China

How to cultivate innovative talents has become an important educational issue nowadays.In China’s long-term mentorship education environment,supervisor-student relationship often affects students’creativity.From the perspective of students’psychology,we explore the influence mechanism of supervisor-student relationship on creativity by machine learning and questionnaire survey.In Study 1,based on video interviews with 16 postgraduate students,we use the machine learning method to analyze the emotional states exhibited by the postgraduate students in the videos when associating them with the supervisor-student interaction scenario,finding that students have negative emotions in bad supervisor-student relationship.Subsequently,we further explore the impact of supervisor-student relationship on postgraduate students’development in supervisor-student interaction scenarios at the affective level.In Study 2,a questionnaire survey is conducted to explore the relationship between relevant variables,finding that a good supervisor-student relationship can significantly reduce power stereotype threat,decrease emotional labor surface behaviors,and promote creativity expression.The above results theoretically reveal the internal psychological processes by which supervisor-student relationship affects creativity,and have important implications for reducing emotional labor and enhancing creativity expression of postgraduate students.

关键词： supervisor-student relationship power stereotype threat emotional labor machine learning computable perception

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