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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: International Journal of Advanced Computer Science and Applications

سال: 2022

ISSN: ['2158-107X', '2156-5570']

DOI: https://doi.org/10.14569/ijacsa.2022.0130558