文章基本信息

标题：Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques
本地全文：下载
作者：Ralph Olusola Aluko ; Olumide Afolarin Adenuga ; Patricia Omega Kukoyi 等
期刊名称：Australasian Journal of Construction Economics and Building
印刷版ISSN：1835-6354
电子版ISSN：1837-9133
出版年度：2016
卷号：16
期号：4
页码：86-98
DOI：10.5130/AJCEB.v16i4.5184
语种：English
出版社：University of Technology, Sydney
摘要：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.
关键词：Academic achievement; architecture students; classification; k-NN; prior academic performance; selection criteria