International journal of


EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
line decor

 Volume 5, Issue 10 (October 2018), Pages: 1-6


 Original Research Paper

 Title: Analysis of the inter-relationship between students’ first year results and their final graduating grades

 Author(s): Pelumi E. Oguntunde 1, *, Hilary I. Okagbue 1, Omoleye A. Oguntunde 2, Abiodun A. Opanuga 1, Sola J. Oluwatunde 3


 1Department of Mathematics, Covenant University, Ota, Nigeria
 2Department of Economics and Development Studies, Covenant University, Ota, Nigeria
 3Department of Computer Science, Caleb University, Lagos State, Nigeria

 Full Text - PDF          XML


There is a tendency for students to lose focus in tertiary institutions because of change in environment and peer pressure (among several others); hence, a need to monitor and study the trend of students’ performance in the tertiary institution. This article, therefore, seeks to know the correlation between the first year results and in particular, the final graduating grade of students in a leading Nigerian University. Test of normality was performed for the final graduating results and multiple linear regression models were fitted to the data; this enables us to predict what a student can graduate with having known a previous result (or first year result). All the analyses were performed using Minitab software. The result established that there are strong linear relationships between the GPAs as a student progresses in his/her academic journey. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Academic performance, Students, University, Multiple linear regressions

 Article History: Received 22 March 2018, Received in revised form 22 July 2018, Accepted 1 August 2018

 Digital Object Identifier:


 Oguntunde PE, Okagbue HI, and Oguntunde OA et al. (2018). Analysis of the inter-relationship between students’ first year results and their final graduating grades. International Journal of Advanced and Applied Sciences, 5(10): 1-6

 Permanent Link:


 References (16) 

  1. Afolabi AO, Mabayoje VO, Togun VA, Oyadeyi AS, and Raji Y (2007). The effect of mode of entry into medical school on performance in the first two years. Journal of Medical Sciences, 7(6): 1021-1026.   [Google Scholar]
  1. Alfan E and Othman N (2005). Undergraduate students' performance: The case of University of Malaya. Quality Assurance in Education, 13(4): 329-343.   [Google Scholar]
  1. Arsad PM and Buniyamin N (2014). Neural network and linear regression methods for prediction of students' academic achievement. In the IEEE Global Engineering Education Conference, IEEE, Istanbul, Turkey: 916-921.   [Google Scholar]
  1. Bamgboye EA, Ogunnowo BE, Badru OB, and Adewoye EO (2001). Students admission grades and their performance at Ibadan University pre-clinical MBBS examinations. African Journal of Medicine and Medical Sciences, 30(3): 207-211.   [Google Scholar] PMid:14510130
  1. Eng TH, Daneil ILA, Kamaruddin SFB, and Rijeng JSA (2017). High school academic performance and academic success in university. Advanced Science Letters, 23(8): 7653-7656.   [Google Scholar]
  1. Gbore LO (2013). Relationship between cognitive entry characteristics and the academic performance of university undergraduates in South West, Nigeria. Nigeria Journal of Educational and Social Research, 3(1): 19-24.   [Google Scholar]
  1. Halde RR, Deshpande A, and Mahajan A (2016). Psychology assisted prediction of academic performance using machine learning. In the IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, IEEE, Bangalore, India: 431-435.   [Google Scholar]
  1. Jaafar R, Bais B, Zaki WMDW, Bukhori MF, Shaarani MFAS, and Huddin AB (2016). Correlation study of student achievement at pre-university level and their corresponding achievement in the year-one undergraduate course of circuit theory at UKM. Pertanika Journal of Social Sciences and Humanities, 24: 87-96.   [Google Scholar]
  1. Kolawole EB and Ilugbusi AA (2007). Cognitive entry grade as predictors of students' academic performance in mathematics in Nigeria universities. Medwell Journal, 2(3): 322-326.   [Google Scholar]
  1. Obemeata JO (1974). The predictive validity of intelligence tests M, ML and MQ. African Journal of Educational Research, 1(2): 205-211.   [Google Scholar]
  1. Odukoya JA, Adekeye O, Igbinoba AO, and Afolabi A (2018a). Item analysis of university-wide multiple choice objective examinations: The experience of a Nigerian private university. Quality and Quantity, 52(3): 983-997.   [Google Scholar]  PMid:29670303 PMCid:PMC5897464
  1. Odukoya JA, Omole DO, Atayero AA, Badejo J, Popoola S, John M, and Ucheaga E (2018b). Learning attributes of summa cum laude students: Experience of a Nigerian university. Cogent Education, 5(1): 1426675.   [Google Scholar]
  1. Odukoya JA, Popoola SI, Atayero AA, Omole DO, Badejo JA, John TM, and Olowo OO (2018c). Learning analytics: Dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university. Data in Brief, 17: 998-1014.   [Google Scholar]  PMid:29876456 PMCid:PMC5988507
  1. Ohuche RO (1974). Academic achievement of Nigerian undergraduates as a function of previous educational experiences. West African Journal of Education, 18(2): 111-115.   [Google Scholar]
  1. Popoola SI, Atayero AA, Badejo JA, John TM, Odukoya JA, and Omole DO (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in a Nigerian Private University. Data in Brief, 17: 76-94.    [Google Scholar] PMid:29876377 PMCid:PMC5988220
  1. Salahdeen HM and Murtala BA (2005). Relationship between admission grades and performances of students in the first professional examination in a new medical school. African Journal of Biomedical Research, 8(1): 51-57.   [Google Scholar]