Design And Development Of A Hybrid Machine Learning Framework For Student Academic Performance Prediction
DOI:
https://doi.org/10.29284/z4s8yb69Keywords:
Educational Data Mining, Machine Learning, Ensemble Learning, PCA, Prediction.Abstract
The timely intervention of students and decision-making process will be largely dependent on proper forecasting of the success of the students in academics. The research in this paper proposes a hybrid machine learning model, which involves a combination of a mix of the polynomial feature expansion, Principal Component Analysis (PCA) and stacking ensemble learning in the prediction of final CGPA of students. This model has been applied on model of 2,288 student records of real-life dataset, comparison with three benchmark models, like, the Random Forest, Support Vector Regressor and Decision Tree. The findings of the experimental research are that the hybrid model was highly efficient compared to the individual approaches and obtained high R2 at 0.902, such a minor error of prediction, and an accurate and consistent classification. According to the results, the hybrid learning can calculate intricate patterns of learning data and remain robust and generalised.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
