Abstract
Purpose – The issues in the current Built Environment Higher Education (BEHE) curricula recognise a critical need for enhancing the quality of teaching. This paper aims to identify the need for a best practice in teaching within Built Environment Higher Education (BEHE) curricula and recommend a set of drivers to enhance the current teaching practices in the Built Environment (BE) education. The study focused on section one of the National Student Survey (NSS) – Teaching on my course; with a core focus on improving student satisfaction, making the subject interesting, creating an intellectually stimulating environment, and challenging learners.
Methodology- The research method used in this study is the mixed method, 1.) A document analysis consisting of feedback from undergraduate students, and 2.) A closed-ended questionnaire to the academics in the BEHE context. More than 375 student feedback were analysed to understand the teaching practices in BE and fed forward to developing the closed-ended questionnaire for 23 academics, including a Head of school, a Principal lecturer, Subject leads and lecturers. The data was collected from Architecture, Construction Management, Civil Engineering, Quantity Surveying, and Building surveying disciplines representing BE context. The data obtained from both instruments were analysed with content analysis to develop 24 drivers to enhance quality of teaching. These drivers were then modelled using the Interpretive Structural Modelling (ISM) method to identify their correlation and criticality to NSS section one themes.
Findings – The study revealed 10 independent, 11 dependent and 3 autonomous drivers, facilitating the best teaching practices in BEHE. The study further recommends that the drivers be implemented as illustrated in the level partitioning diagrams under each NSS section one to enhance the quality of teaching in BEHE.
Practical implications: The recommended set of drivers and the level partitioning can be set as a guideline for academics and other academic institutions to enhance quality of teaching. This could be further used to improve student satisfaction and overall NSS results to increase the rankings of academic institutions.
Originality/Value: New knowledge can be recognised with the ISM analysis and level partitioning diagrams of the recommended drivers to assist academics and academic institutions in developing quality of teaching.
Methodology- The research method used in this study is the mixed method, 1.) A document analysis consisting of feedback from undergraduate students, and 2.) A closed-ended questionnaire to the academics in the BEHE context. More than 375 student feedback were analysed to understand the teaching practices in BE and fed forward to developing the closed-ended questionnaire for 23 academics, including a Head of school, a Principal lecturer, Subject leads and lecturers. The data was collected from Architecture, Construction Management, Civil Engineering, Quantity Surveying, and Building surveying disciplines representing BE context. The data obtained from both instruments were analysed with content analysis to develop 24 drivers to enhance quality of teaching. These drivers were then modelled using the Interpretive Structural Modelling (ISM) method to identify their correlation and criticality to NSS section one themes.
Findings – The study revealed 10 independent, 11 dependent and 3 autonomous drivers, facilitating the best teaching practices in BEHE. The study further recommends that the drivers be implemented as illustrated in the level partitioning diagrams under each NSS section one to enhance the quality of teaching in BEHE.
Practical implications: The recommended set of drivers and the level partitioning can be set as a guideline for academics and other academic institutions to enhance quality of teaching. This could be further used to improve student satisfaction and overall NSS results to increase the rankings of academic institutions.
Originality/Value: New knowledge can be recognised with the ISM analysis and level partitioning diagrams of the recommended drivers to assist academics and academic institutions in developing quality of teaching.
| Original language | English |
|---|---|
| Pages (from-to) | 523-538 |
| Number of pages | 16 |
| Journal | Quality Assurance in Education |
| Volume | 30 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Jul 2022 |
Bibliographical note
Funding Information:The data obtained for the below paper was based on a project guided by a steering committee within the University of Wolverhampton, chaired by Professor Mohammed Arif. Among the committee members, credit needs to be given to Dr David Searle, Dr Alaa Hamood and Dr Louise Gyoh for their significant input on the data collection. Furthermore, the student and academic participants at the University of Wolverhampton need recognition for their insightful comments.
Publisher Copyright:
© 2022, Emerald Publishing Limited.
Keywords
- Enhancing teaching quality
- Built environment higher education
- Learning in post-COVID
- National Student Survey (NSS)
- Teaching on my course