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

Main Article Content

Ralph Olusola Aluko
Olumide Afolarin Adenuga
Patricia Omega Kukoyi
Aliu Adebayo Soyingbe
Joseph Oyewale Oyedeji

Abstract

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.

Article Details

Section
Articles (Peer reviewed)
Author Biographies

Ralph Olusola Aluko, Department of Architecture, Olabisi Onabanjo University

Architecture:  PhD Research student

Olumide Afolarin Adenuga, Department of Building, University of Lagos

Construction management: PhD research student

Patricia Omega Kukoyi, Department of Construction Management, Nelson Mandela Metropolitan University, Port Elizabeth

Construction management: PhD research student

Aliu Adebayo Soyingbe, Department of Building, University of Lagos, Lagos

Construction management: PhD research student

Joseph Oyewale Oyedeji, Department of Estate Management, Bells University of Technology, Otta

Estate management: PhD research student