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@article{208268,
        author = {Milind Subhash Limje},
        title = {A Conceptual Machine Learning Framework for Early Prediction of Student Academic Performance and Identification of At-Risk Students},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {1271-1278},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208268},
        abstract = {The early identification of students who may experience academic difficulties is an important challenge for educational institutions. Conventional methods of identifying academically at-risk students often depend on examination results, attendance records, or teacher observations, which may detect problems only after substantial academic difficulties have occurred. Machine learning (ML) provides an opportunity to analyze multiple academic and behavioral indicators and generate early predictions of student performance. This research proposes a conceptual machine learning framework for the early prediction of student academic performance and identification of at-risk students. The proposed framework integrates student demographic, academic, attendance, engagement, and learning-behavior data into a systematic prediction pipeline. Data preprocessing, feature engineering, exploratory analysis, model training, evaluation, and risk classification are incorporated into the framework. Several supervised learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting, can be considered for comparative evaluation. The framework emphasizes not only predictive accuracy but also interpretability, fairness, privacy, and responsible use of predictions. The resulting system can categorize students into different risk levels and provide early information to educators and academic advisors for timely intervention. The proposed framework is intended to support data-driven educational decision-making while ensuring that machine learning predictions complement rather than replace professional judgment.},
        keywords = {Machine Learning, Student Performance Prediction, At-Risk Students, Educational Data Mining, Early Prediction, Academic Analytics, Classification, Learning Analytics},
        month = {September},
        }