Effectiveness of Campus Sitcom Creation and Performance in College Students’ Mental Health Education Based on Big Data Analysis

This research framework addresses the quantitative evaluation of campus sitcom performances in college mental health education by deploying an edge-assisted big data analytics system. Traditional qualitative assessments of creative arts-based therapy often lack real-time, objective metrics. To resolve this limitation, the proposed methodology utilizes resource-constrained edge computing nodes to perform localized, multimodal behavioral analysis during live student performances. The architecture integrates lightweight deep learning models for facial expression recognition, posture analysis, and vocal sentiment classification directly on edge hardware, ensuring data privacy and low-latency processing. Aggregated edge inference results are transmitted to a centralized cloud database, forming a big data repository that models student engagement and emotional transitions over time.

Statistical validation and machine learning classifiers are subsequently applied to this dataset to assess the pedagogical effectiveness of sitcom-based interventions. The research direction provides comprehensive guidance on model optimization, hardware-software co-design for edge deployment, and statistical correlation techniques. Through this structured implementation support and component guidance, researchers can systematically evaluate the intersection of embedded edge intelligence, big data analytics, and behavioral psychology in educational environments.

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