Exploring the accuracy and reliability of machine learning approaches for predicting student performance

• Autorzy: Owaidat Bilal

Identyfikatory

DOI

10.35784/acs-2024-29

• Języki publikacji: EN

Abstrakty

EN

The purpose of this study is to examine the suitability of machine learning (ML) techniques for predicting students’ performance. By analyzing various ML algorithms, the authors assess the accuracy and reliability of these approaches, considering factors such as data quality, feature selection, and model complexity. The findings indicate that certain ML methods are more effective for student performance forecasting, emphasizing the need for a deliberate evaluation of these factors. This study provides significant contributions to the field of education and reinforces the growing use of ML in decision-making and student performance prediction.

Słowa kluczowe

EN

classification; regression; student performance; Machine Learning

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 3
• Strony: 67--84
• Opis fizyczny: Bibliogr. 32 poz., fig., tab.

Twórcy

autor

Owaidat Bilal

• Lebanese International University, Sciences, Computer, Lebanon

• https://orcid.org/0000-0002-4176-2197

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).