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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction

Author 1: Du Xiaoming
Author 2: Chen Ying
Author 3: Zhang Xiaofang
Author 4: Guo Yu

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130558

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

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Abstract: Student academic performance prediction is one of the important works in the teaching management, which can realize accurate management, scientific teaching and personalized learning by mining important features affecting the academic performance and accurately predicting academic. Due to the subjectivity of feature extraction and the randomness of hyperparameters, the accuracy of academic performance prediction needs to be improved. Therefore, in order to improve the accuracy of prediction, an academic prediction method based on Feature Engineering and ensemble learning is proposed, which makes full use of the advantages of random forest in feature extraction and the ability of XGBoost in prediction. Firstly, the feature importance is calculated and ranked by using the random forest method, and the optimal feature subset combined with the forward search strategy. Secondly, the optimal feature subset is input into the XGBoost model for prediction. The sparrow search algorithm is used to optimize the XGBoost hyperparameters to further improve the accuracy of academic prediction. Finally, the performance of the proposed method is verified through the experiments of the public data set. The experimental results show that the academic prediction method designed is better than the single learner prediction method and other integrated learning prediction methods. The accuracy result jumps to 82.4%. It has good prediction performance and can provide support for teachers to teach according to students’ aptitude.

Keywords: Academic performance prediction; feature engineering; ensemble learning; random forest; XGBoost

Du Xiaoming, Chen Ying, Zhang Xiaofang and Guo Yu, “Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130558

@article{Xiaoming2022,
title = {Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130558},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130558},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Du Xiaoming and Chen Ying and Zhang Xiaofang and Guo Yu}
}


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