Evaluation of the scholastic performance of students in 12 programs from a private university in the south-west geopolitical zone in Nigeria

Introduction

Grade Point Average (GPA) and Cumulative Grade Point Average (CGPA) are two concepts usually made mentioned in the education domain and they signified the diverse systems of awarding undergraduates’ scores based upon their educational performances in their different subjects (Quora, 2019). GPA is computed by summing the total scores and then divide by the credits hours offered by the student. CGPA is the average of the GPA of a particular student which he had acquired in an institution in the courses he had offered. It is also computed by summing the scores acquired by students and then dividing by the computation of his overall credit units. GPA is normally computed in a distinct semester while CGPA is computed for the all-inclusive period of a program in an institution and for students to acquire a good CGPA, the good GPA in all the years must have been obtained by students (Quora, 2019).

In the white-collar job market now, there is high competition among young graduates. Academic performance is one indicator that highlights university students’ qualification and this is mostly measured using the cumulative grade point average (CGPA). Most employers use CGPA to screen out candidates searching for jobs, and candidates with a higher CGPA are selected (Yogendra & Andrew, 2017). Therefore, the performance of students (Oladele et al., 2019) in universities should be a concern not only to administrators and educators but also to corporations in the labor market.

Students have to place greater effort in their study to obtain a good grade in order to fulfil the demands of an employer and this makes academic achievement the main factor considered by employers in the recruitment of workers, especially newly graduated students (Yogendra & Andrew, 2017). The objective of the present study is to determine the study year that students perform better academically across 12 programs in a private university in the south-west geopolitical zone in Nigeria. This study observed that there are few references on the impact of GPA on students’ overall performance and that was the gap filled in this study.

Methods

Primary data was extracted from Covenant University’s student database (John et al., 2018). The dataset contains the cumulative grade point averages (CGPA) from the first to the fourth year of study and the overall CGPA of students.

IBM Statistical Package for Social Sciences (IBM 20) was used to analyze the data of the scholastic performance of students in 12 programs at the College of Science and Engineering within the year 2010 to 2014. The statistical methodology includes regression analysis, analysis of variance (ANOVA), and descriptive statistics (Lukman et al., 2018).

Approval to use the data was obtained from the Ethical Committee of Landmark University, which is affiliated with Covenant University.

Results

A total of 12 programs were assessed, which included 2490 students. The frequency distribution of the number of students who attended the twelve (12) programs and their graduation years are depicted in Table 1 and Table 2, respectively. The descriptive statistics are provided in Table 3. The results show that the mean performance of all the students at each of the level is not too different from each other. Figure 1 shows a histogram of the cumulative CGPA of students for the years 2010–2014. The distribution of the data is skewed to the right which shows that a high number of the students have a CGPA that is between 2 and 5. The number of students with a CGPA that is less than 2 is low.

Figure 1. Histogram for students’ cumulative grade point averages between 2010 and 2014 at a private university in Nigeria.

Table 4 shows the correlation matrix of the variables. The variables include CGPA 100 level, CGPA 200 level, CGPA 300 level, CGPA 400 level, CGPA 500 level and the overall CGPA. A strong positive and significant relationships exist between CGPA in the different level and the overall CGPA. The coefficient of determination (R²) in Table 5 shows that the cumulative grade point average in each level explained about 98.1% of the variations in the response variable (the overall CGPA). The F-test shows that the overall regression model is significant (P-value=0.000<0.05). It was also observed that each of the variables has a positive and significant impact on the overall CGPA. The performance of the students in 200 level is more significant (See Table 5). The maximum variance inflation factor shows that none of the variables is correlated (See Table 5). Results show that overall performance of each student depends on their academic performance in each level.

Conclusion

In this report, we have analyzed the performance of students in 12 programs at a private university in Nigeria. From the various analysis carried out, it was observed that a large number of students graduated in 2013, and from the 12 programs students of electrical and electronic engineering have the highest percentage of graduate students. The descriptive statistics show that the mean performance of all the students at each of the level is not too different from each other. The performance of the student at each level is pivotal to their overall CGPA. In conclusion, we strongly recommend the private university to introduce program that will improve the academic performance of students from year one (100 level).

Data availability

Zenodo: Dataset on the academic performance of students in 12 programmes from a private university, http://doi.org/10.5281/zenodo.1482513 (Oluwaseun et al., 2018).