Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model




Hybrid Model, Grade Prediction, Stack Model


With increasing technical procedures, academic institutions are adapting to a data-driven decision-making approach of which grade prediction is an integral part. The purpose of this study is to propose a hybrid model based on a stacking approach and compare its accuracy with those of the individual base models. The model hybridizes K-nearest neighbours, Random forests, XGBoost and multi-layer perceptron networks to improve the accuracy of grade prediction by enabling a combination of strengths of different algorithms for the creation of a more robust and accurate model. The proposed model achieved an average overall accuracy of around 90.9% for 10 epochs, which is significantly higher than that achieved by any of the individual algorithms of the stack. The results demonstrate the improvement of prediction results but using a stacking approach. This study has significant implications for academic institutions which can help them make informed grade predictions for the improvement of student outcomes.


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How to Cite

S. Reddy and S. D. Pande, “Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

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