Predicting graduation performance through transformed admission data and first year grades using multiple regression and ANOVA: A case study of Delta State University

Abstract

Studies on the relationship between students’ previous results, and graduations are usually based on only the undergraduate sessional results. This study conducts a statistical analysis of student graduation patterns, specifically focusing on the relationship between students' academic performance at the time of admission – measured by their West African Examination Council, WAEC –their first-year results, and their overall performance upon graduation. The study explores the use of coding transformation of WAEC grades to a 5-point scale Grade Point Average/Cumulative Grade Point Average, GPA/CGPA. The goal is to determine whether a student's potential at the time of admission and his/her first-year CGPA could predict academic success at graduation. The study followed a well-defined data collection method and employed data analysis techniques including multiple linear regression, analysis of variance, and coefficient of determination. The results of the statistical analysis reveals that a student's performance in WAEC does not significantly affect university graduation outcome; indicating that a student's ability to graduate with an excellent or a poor result is independent on their performance in the WAEC examination. However, the study did find that a student's first-year academic results significantly contributed to his/her university graduation outcome. As a result, the study offered various recommendations on how students can achieve higher graduating grades.