The Impact of Supervisor-Student Relationship on Creativity: An Emotional Mechanism Exploration

The Impact of Supervisor-Student Relationship on Creativity

The supervisor-student relationship plays a crucial role in fostering creativity. When this relationship is harmonious, it becomes a foundation for knowledge transfer and innovation. On the other hand, a negative relationship between supervisor and student can undermine the effects of power stereotype threat. To understand the emotional dynamics within this relationship, researchers conducted two studies.

In Study 1, researchers analyzed emotional features by using the facial emotion detection method. They recorded video frames and used the Multi-Task Convolutional Neural Network (MTCNN) and VGG19 neural network for facial detection and emotional recognition. The findings revealed a significant increase in negative dynamic performance when discussing the supervisor topic. Additionally, a distinct negative mood swing was observed during the transition from self-introduction to the supervisor topic.

In Study 2, the researchers investigated how the supervisor-student relationship influences creativity on an emotional level. They proposed hypotheses to examine the connections between variables such as supervisor-student relationship, power stereotype threat, emotional labor, and creativity. They collected 592 valid questionnaires from postgraduate students and used a Structural Equation Model (SEM) to analyze the data. The results showed that a positive supervisor-student relationship reduced power stereotype threat. Emotional labor, specifically surface acting and deep acting, had a significant impact on creativity.

To enhance creativity, supervisors should focus on emotional mechanisms like power stereotype threat, surface acting, and deep acting. Institutions should also prioritize the training and assessment of postgraduate supervisors. Students, on the other hand, should actively engage in the relationship and express their ideas. Future research should delve into causal relationships and consider other relevant factors to obtain more accurate results.

For more information, you can check out the research paper and reference article. Credit for this research goes to the dedicated researchers involved in the project. Don’t forget to join our ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and cool AI projects.

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