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Abadou, Y, Kettab, R and Ghreib, A (2018) Experimental investigation on the carbonation properties of dune sand ceramic waste mortar. Journal of Engineering, Design and Technology, 16(03), 501–16.

Adamtey, S and Onsarigo, L (2018) Analysis of pipe-bursting construction risks using probability-impact model. Journal of Engineering, Design and Technology, 16(03), 461–77.

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(03), 385–97.

  • Type: Journal Article
  • Keywords: Artificial intelligence; Academic performance; Logistic regression; Decision-making; Support vector machine; Education; Modelling;
  • ISBN/ISSN: 1726-0531
  • URL: https://doi.org/10.1108/JEDT-08-2017-0081
  • 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/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.

Dadashnejad, A and Valmohammadi, C (2018) Investigating the effect of value stream mapping on operational losses: a case study. Journal of Engineering, Design and Technology, 16(03), 478–500.

Faccio, M, Gamberi, M, Nedaei, M and Pilati, F (2018) Technical and economic modelling and evaluation of a water distribution system equipped with an autoclave for industrial production applications. Journal of Engineering, Design and Technology, 16(03), 342–59.

Hassan, O A and Johansson, C (2018) Glued laminated timber and steel beams. Journal of Engineering, Design and Technology, 16(03), 398–417.

Olanrewaju, A and Tan, S Y (2018) An exploration into design criteria for affordable housing in Malaysia. Journal of Engineering, Design and Technology, 16(03), 360–84.

Othman, A A E and Elsaay, H (2018) A learning-based framework adopting post occupancy evaluation for improving the performance of architectural design firms. Journal of Engineering, Design and Technology, 16(03), 418–38.

Owusu-Manu, D, Kukah, A, Edwards, D J, Pärn, E A, El-Gohary, H and Aigbavboa, C (2018) Causal relationships of moral hazard and adverse selection of Ghanaian Public-Private-Partnership (PPP) construction projects. Journal of Engineering, Design and Technology, 16(03), 439–60.