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@article{179752,
        author = {R. SHIVA SHANKAR and T. PAVAN KUMAR and V. MANOJ RAO and M. JASWANTH and Mr. Dharmendra K Roy},
        title = {Predicting Student Performance and Identifying Academic Challenges Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7911-7916},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179752},
        abstract = {This paper presents a machine learning-based framework to predict student performance and analyze critical factors affecting education during the COVID-19 pandemic. Leveraging historical academic records, the system aims to deliver data-driven insights for targeted interventions, enhancing educational quality and minimizing failure rates. The model identifies key influences such as socioeconomic status and access to technology, while examining the impact of remote learning, reduced participation, and digital inequities. The predictive system provides actionable outputs to inform policy and educational strategies, supporting academic continuity and resilience. This approach promotes equitable, data-informed decision-making to mitigate crisis-driven disruptions in global education},
        keywords = {Academic performance forecasting, Machine learning applications in education, Predictive analytics, Learning environment analysis and Digital divide in education},
        month = {May},
        }