Do job advertisements promote gender inequality in the construction sector?

Paper presented at International Conference for Sustainable Ecological Engineering Design for Society (SEEDS)International Conference for Sustainable Ecological Engineering Design for Society (SEEDS), Bristol UWE University, 31 August - 1 Sept 2022.The poor performance of construction projects remains a topical issue in the academic field of construction management. Across the globe, statistical data indicates that the construction sector is male dominated. The observed inequality is linked to conflicts, which is one of the main reasons for the poor performance of construction projects. The current study aims to explore the differences between job adverts for male [construction manager] and female [social worker] dominated sectors of the economy by comparing word usage. Text mining was used to unearth the differences in the content of the job advertisements for these two roles. The findings indicate that masculine words [such as leader] are the most commonly used words in the job adverts for construction manager roles. The findings suggest that the content of job adverts seem to promote gender stereotypes associated with employment in the construction sector. Such gender cues may contribute to the gender differences in the construction workforce. Taken together, these findings suggest that there is a need to embed gender-neutral words in job adverts placed by construction sector

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

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.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.Published versio