Abstract
The study aimed to determine if any of the entry requirements such as Ordinary Level (OL) results, Unified Tertiary Matriculation Examination (UTME) scores or Post-UTME (PUTME) scores could predict an outstanding academic performance of first-year undergraduate students admitted into the Faculty of Science in the Kaduna State University, Kaduna. The study adopted the descriptive research design. A purposive sample of nine hundred and forty-three (943) first-year students constituted the population for the study were drawn from Computer Science, Mathematics and Physics undergraduate degree programmes from the Faculty of Science of the university who were admitted from the 2010/2011 to 2014/2015 academic sessions. The instruments for data collection were OL, UTME and first-year Cumulative Grade Point Average (CGPA) results, which were coded and analysed with the aid of Computational Statistical Package for Social Sciences (SPSS). Pearson Product Moment Correlation (PPMC) Coefficient and Multinomial Logistics Regression (MLR) were the statistics used to answer the four research questions used. The results revealed that with a weak correlation, OL is a good predictor on the CGPA, a dependent variable, for academic performance which holds true for students who are in the CGPA category of '1st class' and '2nd Class Lower' respectively. It concluded that the use of OL and UTME as instruments is not enough to select candidates for admission and therefore recommended that other instruments such as senior secondary school mock examinations need to be included as part of the entry requirements in the admission criteria.
Highlights
Education is an essential issue regarding the development of any country in the world
The primary purpose of this study is to investigate if Ordinary Level (OL) results, Unified Tertiary Matriculation Examination (UTME) and PUTME scores do predict the academic performance among first-year undergraduate students in the Faculty of Science
Based on the analysis and results using Multinomial Logistic Regression (MLR) and Pearson Product Moment Correlation (PPMC) for each programme and each academic session, it is evident that OL, UTME or PUTME could not individually significantly predict the academic performance of students in Faculty of Science
Summary
Education is an essential issue regarding the development of any country in the world. It is a progressive development of knowledge and skills of examinees through stages of teaching and learning at various levels [1]. The number of undergraduate population in Nigerian Universities has increased from 103 in 1948 to an estimated population of 600,000 in 2018 [4]. In 2005, a total number of 409 students were admitted out of which 199 were for the Faculty of Science. In the 2017/2018 academic session, a total number of students admitted was 4,031, and 1,632 was admitted into the Faculty of Science
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: American Journal of Computer Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
