Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis. Rizk, F. H., Mohamed, M. E., Sameh, B., Zaki, A. M., Eid, M. M., & El-kenawy, E. M. In 2024 International Telecommunications Conference (ITC-Egypt), pages 26–31, July, 2024. ![Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.
@inproceedings{rizk_predictive_2024,
title = {Predictive {Modeling} of {Portuguese} {Student} {Performance}: {Comparative} {Machine} {Learning} {Analysis}},
shorttitle = {Predictive {Modeling} of {Portuguese} {Student} {Performance}},
url = {https://ieeexplore.ieee.org/document/10620557},
doi = {10.1109/ITC-Egypt61547.2024.10620557},
abstract = {Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.},
urldate = {2024-08-17},
booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
author = {Rizk, Faris H. and Mohamed, Mahmoud Elshabrawy and Sameh, Basant and Zaki, Ahmed Mohamed and Eid, Marwa M. and El-kenawy, El-Sayed M.},
month = jul,
year = {2024},
keywords = {Machine learning, Student Performance, machine learning, Machine learning algorithms, Data models, Predictive models, Measurement, Education, Market research, Portuguese secondary education, predicting model},
pages = {26--31},
file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\36YZ5GND\\10620557.html:text/html},
}
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