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

Construction Economics and Building

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Title Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques
 
Creator Aluko, Ralph Olusola
Adenuga, Olumide Afolarin
Kukoyi, Patricia Omega
Soyingbe, Aliu Adebayo
Oyedeji, Joseph Oyewale
 
Subject Construction Education
Academic achievement, architecture students, classification, k-NN, prior academic performance, selection criteria
Academic performance
 
Description 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.
 
Publisher UTS ePRESS
 
Contributor An earlier version of this paper was presented at an international conference held at the Nelson Mandela Metropolitan University in 2016. The authors are grateful to conference participants for their comments. Ralph O. Aluko appreciates Olalekan Oshodi fo
 
Date 2016-12-08
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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Format application/pdf
text/html
 
Identifier http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/5184
10.5130/AJCEB.v16i4.5184
 
Source Construction Economics and Building; Vol 16, No 4 (2016): Construction Economics and Building; 86-98
2204-9029
 
Language eng
 
Relation http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/5184/5728
http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/5184/5748
 
Coverage Nigeria
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Prior academic acheivement
 
Rights Copyright (c) 2016 Ralph Olusola Aluko, Olumide Afolarin Adenuga, Patricia Omega Kukoyi, Aliu Adebayo Soyingbe, Joseph Oyewale Oyedeji
http://creativecommons.org/licenses/by/4.0