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Towards reliable prediction of academic performance of architecture students using data mining techniques
Aluko, Ralph O. Daniel, Emmanuel I. Shamisdeen Oshodi, Olalekan Aigbavboa, Clinton O. Akinsola, Abiodun Olanrewaju
Aluko, Ralph O.
Daniel, Emmanuel I.
Shamisdeen Oshodi, Olalekan
Aigbavboa, Clinton O.
Akinsola, Abiodun Olanrewaju
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2018-06-04
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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.
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, 16(3), pp. 385-397. https://doi.org/10.1108/JEDT-08-2017-0081
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Journal article
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en
Description
This is an accepted manuscript of an article published by Emerald in Journal of Engineering, Design and Technology on 04/06/2018, available online: https://doi.org/10.1108/JEDT-08-2017-0081
The accepted version of the publication may differ from the final published version.
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1726-0531