Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques
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 stud...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
UTS ePRESS
2016-12-01
|
Series: | Construction Economics and Building |
Subjects: | |
Online Access: | https://learning-analytics.info/journals/index.php/AJCEB/article/view/5184 |
id |
doaj-1f1bfbb477e64166b77fad6c21901e31 |
---|---|
record_format |
Article |
spelling |
doaj-1f1bfbb477e64166b77fad6c21901e312020-11-24T21:15:36ZengUTS ePRESSConstruction Economics and Building2204-90292016-12-0116410.5130/AJCEB.v16i4.51843229Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniquesRalph Olusola Aluko0Olumide Afolarin Adenuga1Patricia Omega Kukoyi2Aliu Adebayo Soyingbe3Joseph Oyewale Oyedeji4Department of Architecture, Olabisi Onabanjo UniversityDepartment of Building, University of LagosDepartment of Construction Management, Nelson Mandela Metropolitan University, Port ElizabethDepartment of Building, University of Lagos, LagosDepartment of Estate Management, Bells University of Technology, OttaIn 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.https://learning-analytics.info/journals/index.php/AJCEB/article/view/5184Academic achievementarchitecture studentsclassificationk-NNprior academic performanceselection criteria |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ralph Olusola Aluko Olumide Afolarin Adenuga Patricia Omega Kukoyi Aliu Adebayo Soyingbe Joseph Oyewale Oyedeji |
spellingShingle |
Ralph Olusola Aluko Olumide Afolarin Adenuga Patricia Omega Kukoyi Aliu Adebayo Soyingbe Joseph Oyewale Oyedeji Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques Construction Economics and Building Academic achievement architecture students classification k-NN prior academic performance selection criteria |
author_facet |
Ralph Olusola Aluko Olumide Afolarin Adenuga Patricia Omega Kukoyi Aliu Adebayo Soyingbe Joseph Oyewale Oyedeji |
author_sort |
Ralph Olusola Aluko |
title |
Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
title_short |
Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
title_full |
Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
title_fullStr |
Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
title_full_unstemmed |
Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
title_sort |
predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques |
publisher |
UTS ePRESS |
series |
Construction Economics and Building |
issn |
2204-9029 |
publishDate |
2016-12-01 |
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. |
topic |
Academic achievement architecture students classification k-NN prior academic performance selection criteria |
url |
https://learning-analytics.info/journals/index.php/AJCEB/article/view/5184 |
work_keys_str_mv |
AT ralpholusolaaluko predictingtheacademicsuccessofarchitecturestudentsbypreenrolmentrequirementusingmachinelearningtechniques AT olumideafolarinadenuga predictingtheacademicsuccessofarchitecturestudentsbypreenrolmentrequirementusingmachinelearningtechniques AT patriciaomegakukoyi predictingtheacademicsuccessofarchitecturestudentsbypreenrolmentrequirementusingmachinelearningtechniques AT aliuadebayosoyingbe predictingtheacademicsuccessofarchitecturestudentsbypreenrolmentrequirementusingmachinelearningtechniques AT josephoyewaleoyedeji predictingtheacademicsuccessofarchitecturestudentsbypreenrolmentrequirementusingmachinelearningtechniques |
_version_ |
1716744690023268352 |