Journal of Artificial Intelligence and Metaheuristics

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https://doi.org/10.54216/JAIM

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Volume 7 , Issue 1 , PP: 19-37, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning

Faris H. Rizk 1 * , Mahmoud Elshabrawy 2 , Basant Sameh 3 , Karim Mohamed 4 , Ahmed Mohamed Zaki 5

  • 1 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (faris.rizk@jcsis.org)
  • 2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (CH1900052@dhiet.edu.eg)
  • 3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (CH1900072@dhiet.edu.eg)
  • 4 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (CH1900193@dhiet.edu.eg)
  • 5 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (azaki@jcsis.org)
  • Doi: https://doi.org/10.54216/JAIM.070102

    Received: April 27, 2023 Revised: August 11, 2023 Accepted: January 01, 2024
    Abstract

    This paper deals with a pivotal part of educational data analytics, aiming to increase the accuracy and interpretability of student performance prediction models. The cornerstone of our method is the innovative application of binary waterwheel plant algorithm bWWPA in the feature selection. As we can see, an essential part of any model is the predicted values, which correctly define all the characteristics of this model. Practically, we begin with solid data pre-processing, which incorporates data cleaning and missing values, duplicate removal, and data transformation in order to get model input as optimally as possible. Preceding the application of bWWPA, we employ an ensemble of regression machine learning models. Set up a baseline for predictive capability, getting initial outcomes with an average Mean Squared Error (MSE) of 0.064. The following feature selection phase proceeds, showing the algorithm. Ability to recognize important elements and, as a result, improve model effectiveness and explain power. The comparative analyses after feature selection point to refined gains in the model, and the performance is reporting a lower MSE of 0.032 with the refined models. These findings, methodologically, add to student performance prediction. Accordingly, it emphasizes the decisive status of feature selection in improving models. The paper's significance extends to teachers, institutions, and researchers, giving insights into more precise and relevant student success-supporting interventions.

    Keywords :

    feature selection , student performance prediction , Optimization , educational data analysis , regression models , Waterwheel Plant Optimization Algorithm

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    Cite This Article As :
    H., Faris. , Elshabrawy, Mahmoud. , Sameh, Basant. , Mohamed, Karim. , Mohamed, Ahmed. Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , 2024, pp. 19-37. DOI: https://doi.org/10.54216/JAIM.070102
    H., F. Elshabrawy, M. Sameh, B. Mohamed, K. Mohamed, A. (2024). Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning. Journal of Artificial Intelligence and Metaheuristics, (), 19-37. DOI: https://doi.org/10.54216/JAIM.070102
    H., Faris. Elshabrawy, Mahmoud. Sameh, Basant. Mohamed, Karim. Mohamed, Ahmed. Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning. Journal of Artificial Intelligence and Metaheuristics , no. (2024): 19-37. DOI: https://doi.org/10.54216/JAIM.070102
    H., F. , Elshabrawy, M. , Sameh, B. , Mohamed, K. , Mohamed, A. (2024) . Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning. Journal of Artificial Intelligence and Metaheuristics , () , 19-37 . DOI: https://doi.org/10.54216/JAIM.070102
    H. F. , Elshabrawy M. , Sameh B. , Mohamed K. , Mohamed A. [2024]. Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning. Journal of Artificial Intelligence and Metaheuristics. (): 19-37. DOI: https://doi.org/10.54216/JAIM.070102
    H., F. Elshabrawy, M. Sameh, B. Mohamed, K. Mohamed, A. "Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 19-37, 2024. DOI: https://doi.org/10.54216/JAIM.070102