Assessing the impact of Ordinary Level Grades on the Cumulative Grade Point Average of First Year University Students (A Factorial Design Approach)

  • Oluwole A. Nuga Bells University of Technology
  • Tolulope O. Adekola Bells University of Technology
  • Abba Zakirai Abdulhamid Bells University of Technology
Keywords: O’ Level Grades, Main Effect Model, Interaction Effect Model, Response Surface Model, First-year CGPA
Share Article:


The purpose of this work is to model the Cumulative Grade Point Average (CGPA) of first year engineering students of a private university in southwestern Nigeria using the Ordinary level (O’ level) grades in mathematics, physics, and chemistry as factors. The choice of the three subjects was due to the fact that virtually all the courses taken in first year by engineering students require a solid background in these three subjects. Duplicate samples were randomly selected from a population stratified into all possible factor levels The O’ level grades were converted to scaled variables (as typically done in factorial design) and used as the model matrix of six levels of a three-factorial design. Three orthogonal statistical models were fitted namely; first order, interaction and response surface models using the Ordinary Least Squares (OLS) Estimators. The model of best fit was identified and used to obtain the combination of ordinary level grades that maximized and minimized first year CGPA. The results showed that the three models were statistically significant with each having p-value < 0.001. Response Surface Model provided a better fit in terms of the R^2=47.0% and the RMSE =0.320. The combination of grades that maximizes and minimizes first year CGPA were A1 in all the three subjects and A2 in mathematics, C6 in physics and chemistry, respectively. The results of this work suggested that a large percentage of extraneous factors is affecting the CGPA of first year engineering students in this university due to the relatively small values of the coefficient of determination returned by the three models


Download data is not yet available.


Adeniyi, O. S., Araoye M. A., Amali E. O., Eru E. U., Ojabo C. O. & Alao O.O (2010) Afr. J. Biomed. Res. 13;189 -195

Adelman, C. (1999). Answers in the tool box: Academic intensity, attendance patterns, and bachelor’s degree attainment. Washington, DC: U.S. Department of Education, Office of Educational Research, and Improvement.

Afolabi, A. O., Mabayoje V. O., Togun V. A. & Oyadeyi A. S. (2007) Selection Criteria for entry into the Medical Programme of Nigerian Universities: African Journal of Biomedical Research, Vol. 10, 203 – 209.

Aru, O. E., Achumba I. E. & Opara F. K. (2010). Assessment of The Admission Criteria that Students’ Academic Performance in Undergraduate Years in a Nigerian University. Int'l Conf. Artificial Intelligence. CSREA Press. ISBN: 1-60132-438-3.

Azeez, T.O, Awe, A.C. & Omosebi, P.A. (2018). Predicting Students' Graduating Cumulative Grade Point Average Using Difference Level, Classification and Regression Tree and Linear Regression Algorithm. Journal of Science and Logics in ICT Research. Vol.2, No. 1. 40-47.

Fagoyinbo, I. S., Ajibode I. A., & Olaniran Y.O.A (2014). The Application of Logistic Regression Analysis to the Cumulative Grade Point Average of Graduating Students: A Case Study of Students of Applied Science, Federal Polytechnic, Ilaro. Developing Country Studies. Vol.4, No. 23.

Goos, P. & Jones, B. (2011). Optimal Design of Experiments: A Case-Study Approach. John Wiley and Sons.

Huang, Shaobo & Fang, Ning (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education. 61. 133–145.

Ishitani, Terry & DesJardins, Stephen. (2002). A Longitudinal Investigation of Dropout from College in the United States. Journal of College Student Retention: Research, Theory and Practice. Vol., 4. No 2. 173-201.

Kolajo, T. & Kolajo J. O. (2015). Career Guidance through Admission Procedures in Nigerian Universities Using Artificial Neural Networks. International Journal of Advanced Research in Computer Science and Software Engineering. 5: 9.

Kolawole, E. B., Oginni, O. I. & Fayomi, E. O. (2011). Ordinary Level as Results Predictors of Students' Academic Performance in Chemistry in Nigerian Universities. Educational Research and Reviews Vol. 6(16), pp. 889-892

Mashael, A. Al-Barrak & Muna Al-Razgan (2016). Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology, Vol. 6, No. 7.

Owolabi, F, Oguntunde P., Adetula, D & Fakile, S. (2016). Learning Analytics: Datasets on the academic record of accounting students in a Nigerian University. Data in Brief. 19. 10

Oyebola, D. D. O. (2006) The Importance of „O‟ level grades in Medical School Admission. The University of Ado-Ekiti Experience. Afr. J. Biomed. Res 9: 15-21.

Willingham, W. W. (1985). Success in college: The role of personal qualities and academic ability. New York: College Entrance Examination Board.

5 February, 2024
How to Cite
Nuga, O., Adekola, T., & Abdulhamid, A. (2024). Assessing the impact of Ordinary Level Grades on the Cumulative Grade Point Average of First Year University Students (A Factorial Design Approach). East African Journal of Education Studies, 7(1), 144-154.