Pre-enrollment factors and academic performance of first-year science students at a Nigerian university: a multivariate analysis

Young, Abimbola S.

doi:10.1007/bf00138187

Cited by 7 publications

(11 citation statements)

References 21 publications

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“…Linear discriminant analysis was applied because this technique was employed in early studies (e.g. Young, 1989). Also, the k-NN modelling technique has provided useful predictions in different fields of academic endeavours, such as ergonomics (Sánchez et al, 2016); traffic engineering (Yoon and Chang, 2014) and medicine (Zhu et al, 2007) among others.…”

Section: Methodsmentioning

confidence: 99%

“…This has led to development of several theories found in the field of education, such as pedagogical theory, curriculum theory, and learning theory among others. Empirical studies have investigated the impact of a whole range of factors on the academic performance of undergraduate students in universities (Parker et al, 2004;Young, 1989). It is worth noting that the terms 'achievement' and 'academic success' are used interchangeably in the literature.…”

Section: Academic Successmentioning

confidence: 99%

“…The input variables are the grades obtained in the Ordinary ('O') level examination, total score in the University Matriculation Examination (also called JAMB or UTME) and mode of entry (which is a dummy variable 1= Direct Entry and 0 = JAMB). The collected data are similar to those used in an earlier study (see Young, 1989). For the output variable, the cumulative grade point average (CGPA) obtained by each student upon completion of the architecture undergraduate programme was used as a measure of academic success.…”

Section: Datamentioning

confidence: 99%

“…The selection of students by such committees is usually based on prescribed criteria for each programme at the different universities. A robust selection process should ensure that the best students are selected for each programme (Holt et al, 2006;Neame et al, 1992;Young, 1989). Furthermore, the previous academic performance of applicants is crucial information needed to aid the selection process.…”

Section: Introductionmentioning

confidence: 99%

“…prior academic performance and academic success) has a long history (e.g. Young, 1989). Although the findings of previous studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

Aluko

Adenuga

Kukoyi

et al. 2016

CEB

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In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

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