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  <doi_batch_id>aspg-28-3149-1792155079</doi_batch_id>
  <timestamp>20261016125119</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Artificial Intelligence and Metaheuristics</full_title>
    <abbrev_title>JAIM</abbrev_title>
    <issn media_type="electronic">2833-5597</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>7</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Predictive Modeling of Global Educational Outcomes: A Comparative Analysis Using Machine Learning Regression Techniques</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Abdelhameed</given_name>
      <surname>Ibrahim</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Abdelaziz A.</given_name>
      <surname>Abdelhamid</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ehab M.</given_name>
      <surname>Almetwally</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Education contributes a crucial portion to the world’s development; thus, it is crucial to focus on education enrollment and quality education. It is essential not only that children enroll in school but also that they receive proper education to improve individuals and, consequently, society. This paper aims to use machine learning to predict educational outcomes based on the World Educational Data obtained from Kaggle to analyze the data, preprocess it, and evaluate the performances of the different regression models. The following models consist of Support Vector Regression (SVR), CatBoost, RandomForestRegressor, ExtraTreesRegressor, GBoost, MLPRegressor, GradientBoosting Regressor, DecisionTreeRegressor, KNeighborsRegressor, LinearRegression, and Pipeline. Evaluation measures used included MSE, RMSE, MAE, MBE, r, R2, NSE, and WI. Analyzing the performance comparison, the best accuracy was associated with CatBoost with an r value equal to 0.999996 and an R2 value of 0. 999993; The MSE score was 0.04024. The outcomes of the present paper demonstrate that the application of advanced machine learning algorithms can be used effectively to predict educational outcomes, thus enabling policymakers and educational planners to use them for designing effective educational policies and overcoming existing global challenges in the sphere of education.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>62</first_page>
     <last_page>72</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3149</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/JAIM.070206</doi>
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