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

Towards reliable prediction of academic performance of architecture students using data mining... 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/approachThe 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.FindingsThe 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/implicationsAlthough 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/valueThe developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Engineering Design and Technology Emerald Publishing

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

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References (67)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1726-0531
DOI
10.1108/jedt-08-2017-0081
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Journal of Engineering Design and TechnologyEmerald Publishing

Published: Jul 3, 2018

Keywords: Artificial intelligence; Academic performance; Logistic regression; Decision-making; Support vector machine; Education; Modelling

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