Gender-based comparison of students’ academic performance using regression models

ADEKITAN, AI and SHOBAYO, Olamilekan (2020). Gender-based comparison of students’ academic performance using regression models. Engineering and Applied Science Research, 47 (3), 241-248.

[img]
Preview
PDF
Shobayo-GenderBasedComparison(VoR).pdf - Published Version
Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview
Official URL: https://ph01.tci-thaijo.org/index.php/easr/article...
Open Access URL: https://ph01.tci-thaijo.org/index.php/easr/article... (Published)

Abstract

The process required to gain admission into tertiary institutions is challenging for Nigerian students. This is due to the various examinations and requirements that must be met to be qualified for admission among innumerable applicants. It is therefore imperative that after admission, students must pursue academic excellence to justify the opportunity given to them, and this will also improve their chances of success after graduation. In a university, some first-year students struggle because of cultural disadvantages, as well as their economic and social backgrounds. This has resulted in poor performance by some students and inevitably led to bad grades at graduation and some drop out of the university without graduating. Female students are often said to perform a bit poorer in terms of academic performance. This study is a comparative performance analysis of male and female students in Science, Technology, Engineering and Mathematics (STEM), conducted using regression models. Trend analysis shows that in this case study, female students have a tendency to improve on their academic performance from their first to their final year. The highest R-squared value of 0.7069 was achieved based on a regression analysis of the performance of 1,093 female students.

Item Type: Article
Identification Number: https://doi.org/10.14456/easr.2020.27
Page Range: 241-248
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 02 Mar 2022 16:12
Last Modified: 02 Mar 2022 16:15
URI: https://shura.shu.ac.uk/id/eprint/29796

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics