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Forecasting Students' Academic Performance in Educational Data Using Machine Learning Techniques

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Title: Forecasting Students' Academic Performance in Educational Data Using Machine Learning Techniques
Language: English
Authors: Najah Al-Shanableh (ORCID 0000-0001-9877-8782), Mazen S. Alzyoud (ORCID 0000-0003-4729-2103), Ahmed Khalil (ORCID 0000-0002-9082-2280), Mohamed Benlamine (ORCID 0000-0001-9858-5526), Insaf Kraidia, Sadeq Damrah, Atena M. Tabakhi (ORCID 0000-0001-5317-8425)
Source: International Journal of Information and Communication Technology Education. 2026 22(1).
Availability: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Artificial Intelligence, Academic Achievement, Prediction, Mathematics Achievement, Secondary School Students, Foreign Countries, Native Language Instruction, Portuguese, Models, Data Analysis, Predictor Variables, Student Characteristics
Geographic Terms: Portugal
DOI: 10.4018/IJICTE.399756
ISSN: 1550-1876
1550-1337
Abstract: This study benchmarks multiple machine learning models to predict student academic performance. The research analyzes data from students in mathematics and Portuguese language courses, examining the relationship between various factors and academic performance. The benchmark implementation includes data preprocessing, exploratory data analysis, feature engineering, model training, and hyperparameter tuning for both regression (predicting final grades) and classification (predicting pass/fail outcomes) tasks. The findings demonstrate that ensemble methods, particularly gradient boosting models, outperform other algorithms with root mean square error of 3.34 for regression and F1 score of 0.88 for classification after hyperparameter tuning. Feature importance analysis reveals that past failures, alcohol consumption, study time, and parent education level are among the most influential predictors of academic performance. The results provide valuable insights for educational stakeholders to implement targeted interventions for at-risk students and improve overall academic outcomes.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1495137
Database: ERIC
FullText Text:
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  Data: Forecasting Students' Academic Performance in Educational Data Using Machine Learning Techniques
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  Data: <searchLink fieldCode="AR" term="%22Najah+Al-Shanableh%22">Najah Al-Shanableh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9877-8782">0000-0001-9877-8782</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mazen+S%2E+Alzyoud%22">Mazen S. Alzyoud</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4729-2103">0000-0003-4729-2103</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ahmed+Khalil%22">Ahmed Khalil</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9082-2280">0000-0002-9082-2280</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mohamed+Benlamine%22">Mohamed Benlamine</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9858-5526">0000-0001-9858-5526</externalLink>)<br /><searchLink fieldCode="AR" term="%22Insaf+Kraidia%22">Insaf Kraidia</searchLink><br /><searchLink fieldCode="AR" term="%22Sadeq+Damrah%22">Sadeq Damrah</searchLink><br /><searchLink fieldCode="AR" term="%22Atena+M%2E+Tabakhi%22">Atena M. Tabakhi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5317-8425">0000-0001-5317-8425</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Information+and+Communication+Technology+Education%22"><i>International Journal of Information and Communication Technology Education</i></searchLink>. 2026 22(1).
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  Data: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
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  Data: 23
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  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Native+Language+Instruction%22">Native Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Portuguese%22">Portuguese</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Portugal%22">Portugal</searchLink>
– Name: DOI
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  Data: 10.4018/IJICTE.399756
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  Data: 1550-1876<br />1550-1337
– Name: Abstract
  Label: Abstract
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  Data: This study benchmarks multiple machine learning models to predict student academic performance. The research analyzes data from students in mathematics and Portuguese language courses, examining the relationship between various factors and academic performance. The benchmark implementation includes data preprocessing, exploratory data analysis, feature engineering, model training, and hyperparameter tuning for both regression (predicting final grades) and classification (predicting pass/fail outcomes) tasks. The findings demonstrate that ensemble methods, particularly gradient boosting models, outperform other algorithms with root mean square error of 3.34 for regression and F1 score of 0.88 for classification after hyperparameter tuning. Feature importance analysis reveals that past failures, alcohol consumption, study time, and parent education level are among the most influential predictors of academic performance. The results provide valuable insights for educational stakeholders to implement targeted interventions for at-risk students and improve overall academic outcomes.
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  Data: As Provided
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  Label: Entry Date
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  Data: 2026
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  Label: Accession Number
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  Data: EJ1495137
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        Value: 10.4018/IJICTE.399756
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Mathematics Achievement
        Type: general
      – SubjectFull: Secondary School Students
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Native Language Instruction
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      – SubjectFull: Portuguese
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      – SubjectFull: Models
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Predictor Variables
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      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Portugal
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      – TitleFull: Forecasting Students' Academic Performance in Educational Data Using Machine Learning Techniques
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            NameFull: Najah Al-Shanableh
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              Y: 2026
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              Value: 1550-1876
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