Data driven 3-D animation for student group collaboration

This project investigates the critical factors influencing group collaboration in educational settings through data analysis and visualization techniques. By analyzing student questionnaire data using machine learning models - including decision trees, random forests, and gradient boosting - three key determinants of teamwork effectiveness were identified: communication efficiency, workload distribution, and leadership style. The findings reveal that balanced task allocation and clear leadership significantly enhance collaboration satisfaction, while communication barriers often lead to negative outcomes. Based on these insights, a 3D animation tool was developed using Blender to visually represent dynamic team interactions, such as discussions and conflicts. Additionally, the study explores the potential of AI-generated animation (via the Keling AI platform) for a comparative study. It has been found that the AI-generated animations only expand the movement on the basis of the input images thus fails to tell the stories learned from the collected datasets. The integration of data-driven analysis with animation not only provides educators with actionable insights to optimize instructional design but also demonstrates the complementary roles in educational technology.