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@article{189470,
        author = {Pratiksha Rohom and Rajashree Jadhav and Vaishnavi Wakchaure and Priyanka Jadhav},
        title = {A Data Analytics Approach to Student Academic Performance Evaluation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {6549-6551},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189470},
        abstract = {The evaluation of student academic performance is essential for maintaining and improving the quality of higher education. Conventional evaluation methods mainly focus on pass–fail statistics and overall grades, which provide limited insight into learning outcomes. With the growing availability of academic data, data analytics offers effective techniques to analyze student performance systematically. This research proposes data analytics–based approach to evaluate student academic performance using historical examination result data. Statistical analysis and visualization techniques are applied to identify trends, subject-wise performance, and correlations between internal assessment and final examination results. The study highlights the importance of data-driven academic decision-making for continuous quality improvement.},
        keywords = {Data Analytics, Student Academic Performance, Result Analysis, Educational Data Mining, Learning Analytics},
        month = {December},
        }