Academic Stress and Emotional Resilience among University Students
DOI:
https://doi.org/10.63345/Keywords:
Academic Stress, Emotional Resilience, University Students, Machine Learning, Digital Mental Health, Explainable Artificial IntelligenceAbstract
Academic stress is increasingly recognized as a significant challenge to university students’ psychological functioning and academic adjustment.
This study examines emotional resilience as a dynamic protective capacity that may explain why students exposed to comparable academic demands experience substantially different levels of stress. A key research gap is the limited integration of psychological resilience indicators with behavioral and academic features in computational models designed to identify students vulnerable to elevated academic stress.
The proposed study develops a machine-learning framework that models academic stress while explicitly incorporating emotional resilience, sleep regularity, perceived workload, coping behavior, social support, and patterns of digital academic engagement. Rather than treating resilience only as a correlational variable, the framework evaluates its contribution to stress-risk classification and individual differences in adaptation.
Explainable machine-learning techniques are incorporated to determine which combinations of resilience and behavioral characteristics contribute most strongly to predicted stress vulnerability.
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