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Towards reliable prediction of academic performance of architecture students using data mining techniques

Ralph Olusola Aluko (Department of Architecture, Olabisi Onabanjo University, Ago Iwoye, Nigeria)
Emmanuel Itodo Daniel (Department of Construction Management and the Built Environment, Southampton Solent University, Southampton, UK)
Olalekan Shamsideen Oshodi (Department of Construction Management and Quantity Surveying, University of Johannesburg, Johannesburg, South Africa)
Clinton Ohis Aigbavboa (Department of Construction Management and Quantity Surveying, University of Johannesburg, Johannesburg, South Africa)
Abiodun Olatunji Abisuga (Department of Construction Management and Property, University of New South Wales, Randwick, Australia)

Journal of Engineering, Design and Technology

ISSN: 1726-0531

Article publication date: 26 April 2018

Issue publication date: 3 July 2018

690

Abstract

Purpose

In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement.

Design/methodology/approach

The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.

Findings

The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students.

Research limitations/implications

Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.

Originality/value

The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

Keywords

Citation

Aluko, R.O., Daniel, E.I., Shamsideen Oshodi, O., Aigbavboa, C.O. and Abisuga, A.O. (2018), "Towards reliable prediction of academic performance of architecture students using data mining techniques", Journal of Engineering, Design and Technology, Vol. 16 No. 3, pp. 385-397. https://doi.org/10.1108/JEDT-08-2017-0081

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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