EXPLORING THE ACCURACY AND RELIABILITY OF MACHINE LEARNING APPROACHES FOR STUDENT PERFORMANCE

Bilal OWAIDAT

doi:10.35784/acs-2024-29

EXPLORING THE ACCURACY AND RELIABILITY OF MACHINE LEARNING APPROACHES FOR STUDENT PERFORMANCE

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Published: Sep 30, 2024

DOI: https://doi.org/10.35784/acs-2024-29

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DOI

https://doi.org/10.35784/acs-2024-29

Authors

Bilal OWAIDAT

bilal_owaydat@hotmail.com

Lebanese International University

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

Abstract

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.

Keywords:

Classification, Regression, Student Performance, Machine Learning

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Article Details

OWAIDAT, B. (2024). EXPLORING THE ACCURACY AND RELIABILITY OF MACHINE LEARNING APPROACHES FOR STUDENT PERFORMANCE. Applied Computer Science, 20(3), 67–84. https://doi.org/10.35784/acs-2024-29

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