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IJIET 2024 Vol.14(12): 1716-1723
doi: 10.18178/ijiet.2024.14.12.2202

Understanding Student Performance in Foundation Year: Insights from Logistic Regression, Naïve Bayes, and Random Forest Models

Abdallah Bashir Musa
Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Email: abhamad@iau.edu.sa (A.B.M.)

Manuscript received August 20, 2024; revised September 9, 2024; accepted November 4, 2024; published December 13, 2024

Abstract—Foundation programs enhance students’ essential skills, equip them for degree programs, and impact academic performance, retention, and intrinsic motivation. Previous studies focused mostly on demographic factors and statistics. Limited literature has focused on students’ performance in the foundation year. This study uses machine learning techniques to investigate the factors influencing foundation year students’ performance. The study assesses 22 predictor factors, including demographics, secondary school achievement, language proficiency, and university experiences, using Logistic Regression (LR), Naïve Bayes (NB), and Random Forest (RF) algorithms. The study’s findings revealed that gender, school type, secondary school scores, desired college major, and English and math proficiency levels were the significant determinants of students’ performance in their foundation year. Random Forest (RF) showed higher accuracy than both Naïve Bayes (NB) and Logistic Regression (LR). The study indicated that identifying performance factors can improve support services by maximizing learning and results via data-driven methodologies. In conclusion, this study revealed the potential of machine learning in evaluating student performance determinants, supporting targeted interventions, and individualized training through advanced machine learning algorithms and longitudinal data. Moreover, the study helps predict students’ performance in the second semester. Consequently, it projects the enrollment figures for each college along with the anticipated dropout rates.

Keywords—foundation year, Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF)

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Cite: Abdallah Bashir Musa, "Understanding Student Performance in Foundation Year: Insights from Logistic Regression, Naïve Bayes, and Random Forest Models," International Journal of Information and Education Technology vol. 14, no. 12, pp. 1716-1723, 2024.


Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Jon-Chao Hong
  • Managing Editor: Ms. Nancy Y. Liu
  • E-mail: editor@ijiet.org
  • Abstracting/ Indexing: Scopus (CiteScore 2023: 2.8), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • Article Processing Charge: 800 USD

 

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