Predictors of Candidates' Performance in the Unified Tertiary Matriculation Examination (UTME) in South-West Nigeria (2010–2024)
DOI:
https://doi.org/10.64348/zije.2026651Keywords:
predictors of performance, Unified Tertiary Matriculation Examination, computer-based testing.Abstract
This study examined the predictors of candidates' performance in Nigeria's Unified Tertiary Matriculation Examination (UTME) in South-west Nigeria, and the extent to which UTME scores and historical performance patterns predict subsequent academic and examination outcomes, over the period 2010–2024. A retrospective cohort design linked UTME and first-year academic records for 2,250 undergraduates admitted between 2010 and 2024 to six universities (four federal, two state) across Lagos, Oyo, Osun and Ekiti States, supplemented by survey data from 750 teachers and thematic analysis of teacher and computer-based-testing (CBT) centre personnel accounts. The study drew on the Education Production Function, Messick's unified view of validity, Human Capital Theory and Social Reproduction Theory. A researcher-developed instrument, the Assessment of Factors Influencing UTME Performance Questionnaire, showed good internal consistency (α = .84; subscale range .72–.86) and a six-factor structure confirmed by exploratory factor analysis (KMO = .87; Bartlett's p < .001). UTME score was positively associated with first-year CGPA, r = .681, 95% CI [.658, .703], p < .001, representing a substantial share of shared variance between the two measures. A regression of UTME score on background characteristics showed that CBT familiarity, school type, study hours, guardian education and school location were each positively and significantly associated with UTME performance (all p < .001), with gender showing a smaller but significant association (p = .030). Historical UTME performance data also significantly predicted future candidates' performance patterns, F(1, 2248) = 326.28, p < .001, R² = .127, indicating moderate but meaningful forecasting value. Qualitative accounts from teachers and CBT-centre personnel identified digital readiness, family and socioeconomic support, study practices, and examination-centre infrastructure as perceived influences on performance. The findings support the continued use of UTME as one component of admission decisions, but the associations with school context and digital familiarity point to unequal preparation opportunities that raise equity concerns. Contextualised admission benchmarks, multiple admission criteria, CBT familiarisation programmes, and expanded CBT infrastructure are recommended.
References
Abdulkareem, Z., & Lennon, M. (2025). Impact of digital divide on students' performance in computerised UTME in Nigeria. Information Development, 41(4), 1265–1280. https://doi.org/10.1177/02666669231191734 DOI: https://doi.org/10.1177/02666669231191734
Adedayo, T. G. (2018). Relationship between post-UTME scores and students' academic performance in Tai Solarin University of Education, Ogun State. African Journal of Educational Management, 19(2), 1–16.
Adegbija, M. V., & Adekoya, A. A. (2020). Digital readiness and students' performance in computer-based UTME in Nigeria. Nigerian Journal of Educational Technology, 5(2), 34–45.
Adeniyi, O. S., Araoye, M. A., Amali, E. O., Eru, E. U., Ojabo, C. O., & Alao, O. O. (2010). Effect of using a combination of O'level results with JAMB score on student performance in the first two years of medical school in Benue State University, Makurdi. African Journal of Biomedical Research, 13(3), 189–195.
Adeyemi, B. A. (2017). A longitudinal study of candidates' performance in the UTME. Journal of Educational Assessment, 15(2), 23–35.
Afolabi, F. O. (2019). Analysing regional disparities in UTME performance in South-west Nigeria (2010–2018). Journal of Educational Assessment in Africa, 11(3), 77–89.
Ajala, O. P. (2010). Three years of post-UME screening: Influence on science education students' achievement at Delta State University, Abraka. International Journal of Educational Sciences, 2(1), 29–40. DOI: https://doi.org/10.1080/09751122.2010.11889998
Aladejana, F. O., & Ojedokun, O. (2012). Challenges in Nigerian education: Implications for policy and practice. African Journal of Education and Technology, 2(1), 78–92.
Bamgboye, E. A., Ogunnowo, B. E., Badru, O. B., & Adewoye, E. O. (2001). Students' admission grades and their performance at the Ibadan University preclinical MBBS examinations. African Journal of Medicine and Medical Sciences, 30, 207–211.
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis. Columbia University Press.
Bourdieu, P., & Passeron, J. C. (1977). Reproduction in education, society and culture. Sage.
Hanushek, E. A. (1986). The economics of schooling: Production and efficiency in public schools. Journal of Economic Literature, 24(3), 1141–1177.
Haruna, J., Yusuf, B., Adamu, U. A., & Bello, A. (2024). Relationship between students' UTME scores and their first-year academic achievement. Gombe Journal of Education, 7(1), 17–30.
Idris, A., & Lawal, M. (2021). Gender disparities in UTME results in South-west Nigeria. African Journal of Educational Management, 15(2), 76–94.
Joint Admissions and Matriculation Board. (2010–2024). Annual reports and statistical digests. JAMB Press.
Joint Admissions and Matriculation Board. (2024). 2024 UTME results and examination statistics. JAMB Bulletin, 6 May 2024.
Kale, O. O. (2004). An educational system in decline. Odua Printing and Publishing Company.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001). A comprehensive meta-analysis of the predictive validity of the Graduate Record Examinations. Psychological Bulletin, 127(1), 162–181. DOI: https://doi.org/10.1037/0033-2909.127.1.162
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). American Council on Education/Macmillan.
Olafogha, T. A., Oladele, B. J., & Wright, E. M. (2023). Predictors of academic success in standardised testing: A study of Nigerian high schools. International Journal of Educational Policy Studies, 18(3), 91–110.
Olatunji, S. O., & Fashiku, C. O. (2020). Impact of school location on academic achievement of secondary school students in Nigeria. Global Journal of Educational Research, 19(1), 1–10.
Sackett, P. R., Kuncel, N. R., Beatty, A. S., Rigdon, J. L., Shen, W., & Kiger, T. B. (2012). The role of socioeconomic status in SAT–grade relationships and in college admissions decisions. Psychological Science, 23(9), 1000–1007. DOI: https://doi.org/10.1177/0956797612438732
Salahdeen, H. M., & Murtala, A. B. (2005). Relationship between admission grades and performance of students in the first professional examination in a new medical school. African Journal of Biomedical Research, 8, 51–57. DOI: https://doi.org/10.4314/ajbr.v8i1.35760
Uko, E. S., & Nnadi, P. O. (2017). The role of gender and socio-economic status in academic performance in the UTME. Nigerian Journal of Educational Foundations, 20(1), 91–105.
Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper and Row.
Zhenseh, A. J., & Ogunode, N. J. (2022). Barriers to educational development in Nigeria: Implications for policy and practice. International Journal of Research and Innovation in Social Science, 6(4), 432–439.
Zwick, R. (2002). Fair game? The use of standardized admissions tests in higher education. RoutledgeFalmer DOI: https://doi.org/10.1037/e672192007-009



