The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student 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 was 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 are good predictors of academic performance of architecture students. 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 a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

Evaluation of factors affecting professional services in building projects in Nigeria

This study is aimed at investigating the main factors affecting professional services in the building industry towards achieving improvement of service delivery by consultancy firms. Questionnaires were distributed to 120(one hundred and twenty (120) respondents to elicit information about their perceptions of the factors affecting professional services. A total of 9 main factors and 39 sub-variables were used to design the questionnaire. The respondents were chosen based on their involvement in an on-going building projects. Therefore, purposive sampling was used to select the population of the study. One hundred and six (106) questionnaires were returned for analysis. The Mean Item Score(MS) was used to rank the factors as perceived by architects, engineers and quantity surveyors. The spearman rank order was calculated and used to test the hypothesis which states that there is no significant difference between the perceptions of the different group of professionals. The results showed that there is an agreement in the ranking with a strong relationship among the professionals. Ability of the client to choose the right design team is the most important factor in delivering quality service. Other important factors are staff motivation and training, commitment of members of staff of the design team, long term potential relationship with client organization, adequate authority for the design team to perform effectively and the client’s financial position. The result will guide both client and practitioners about those factors that need to be focused to improve service delivery

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

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

Predicting academic success of undergraduate architecture students : using k nearest neighbour algorithm

Abstract: The number of applicants considered during the admission selection process for universities has increased exponentially. This has led to development and improvement of the admission criteria so as to ensure that new intakes that possess the potential to achieve academic success are selected. The aim of the present study is to examine the relationship between academic success of architecture student and prior academic performance using K-nearest neighbour algorithm (k-NN). Data on prior academic performance, which is considered during admission process, and academic success was collected on four cohorts of architecture students. Then the data is divided into two parts: training set (70%) and test set (30%). Finally, the k-NN was developed using the training sets and the predictive performance was evaluated using the test set. The experimental results shows that the overall accuracy of the k-NN model is 73.33%. It is anticipated that the developed model could provide useful information that can be used to identify new intakes whom possess adequate intellectual capabilities to succeed in undergraduate architecture programs in Nigeria