Tag Archives: Predictive

Unified Tertiary Matriculation Examination (UTME) and the Post Unified Tertiary Matriculation Examination (PUTME) As Predictors of Undergraduate Students’ Final Grades (Published)

This study set out to investigate UTME and PUTME Examinations as predictors of undergraduate students’ final grades. The research design is the ex-post-facto. The population of this study comprises of 100 and 200level undergraduate students in four different departments. The sample for the analysis is 436 students. Data were collected from the official students’ records at the Management Information System (M.I.S) Unit of the University of Benin.  The data collected were analyzed using Pearson Product Moment Correlation Coefficient (r) and linear regression.  The findings of the study revealed that JAMB/UTME, PUTME scores do not significantly predict undergraduate final grades in Nigeria University and both JAMB/UTME and PUME scores combined do not significantly predict undergraduate final grades in Nigeria University.  The need for stakeholders in education to examine the relevance of the JAMB/UTME and PUTME examinations in the selection of students into the tertiary institution which has led to multiplicity of examination and other attendant problems for the students, parents and even the institutions; and the need for every tertiary institution to be allowed to conduct their screening examination and not a stooge of the Joint Admission and Matriculation Board were recommended.

Keywords: : Academic Performance, Examination, Predictive, validity


This paper is a study of two-group classification models for binary variables. Eight classification procedures for binary variables are discussed and evaluated at each of 118 configurations of the sampling experiments. The results obtained ranked the procedures as follows: Optimal, Linear discriminant, Maximum likelihood, Predictive, Dillon Goldstein, Full multinomial, Likelihood and Nearest neighbour. Also the result of the study show that increase in the number of variables improve the accuracy of the models.

Keywords: Binary Variables, Classification Models, Dillon Goldstein, Linear Discriminant, Misclassification, Optimal, Predictive, and Multinomial, maximum likelihood