Predictive Analytics in Education: Modeling the Complex Relationship Between Learning Modalities and Student Well-being

Introduction: This study examines publication trends related to student stress and mental health during online learning periods, while exploring opportunities for curriculum innovation in post-pandemic education. Method; The research utilizes a Kaggle-sourced dataset comprising survey responses from 1,000 students about their psychological well-being during remote education. This rich dataset includes ten variables capturing demographic information, lifestyle patterns, and self-assessed mental health metrics, providing valuable material for comprehensive data exploration, visualization, and predictive analysis of digital learning's psychological impacts. Beyond immediate stress assessment, the data enables investigation of broader themes including educational technology adaptation, sleep disruption, social isolation, stress perception, and emotional coping mechanisms. Result: The findings highlight the urgent need for educational systems to develop flexible curricula that address both pandemic-era challenges and evolving post-COVID learning environments. Conclusion: The study proposes curriculum frameworks that integrate mental health support with academic content, preparing institutions for future disruptions while promoting student resilience in hybrid learning settings.