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@article{173141,
        author = {Om Motvani and Ankit Kalariya and Nisha Vadodariya},
        title = {A Review On Student Performance Prediction Using Supervised Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2842-2844},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173141},
        abstract = {Assessing student performance is an essential step for achieving academic success and delivering timely support to students who may be at risk. This review article aggregates findings from ten research studies, concentrating on using supervised learning methods to forecast academic results. Various algorithms have been thoroughly examined, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees. This article delves into the techniques used, performs a comparative analysis of different models, addresses challenges faced, and suggests future research directions, underlining the transformative impact of machine learning in the field of education.},
        keywords = {Decision Tree, SVM, CNN, Neural Network.},
        month = {March},
        }