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

Ralph Olusola Aluko, Emmanuel Itodo Daniel, Olalekan Oshodi, Clinton Ohis Aigbavboa, Abiodun Olatunji Abisuga

    Research output: Contribution to journalArticlepeer-review


    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.

    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.

    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.

    The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.
    Original languageEnglish
    Pages (from-to)385-397
    JournalJournal of Engineering, Design and Technology
    Issue number3
    Publication statusPublished - 4 Jun 2018


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