Digital Built Environment and Data Science

From the preparation of the client brief, through conceptual and detailed design, to the construction and operation stages, various digital and data science technologies in integration with BIM processes have provided technical and management support to solve complex and multidisciplinary problems for all project stakeholders; e.g. clients, engineers, managers, contractors, and the supply chain.

The integration of Construction Data Modelling (e.g. BIM and the relevant construction data sharing and exchange technologies) with Data Analytic techniques enables the development of new graduates’ skills which are in high demand for the new normal business practices in construction and built environment.

The findings of this report suggest that the integration of data science in built environment education is still in its infancy, and more needs to be done to ensure that the scope of data science is fully understood and embraced. The current trend of emphasising BIM in MSc programmes is not sufficient to fully integrate data science in built environment education. Other digital technologies need to be incorporated to provide future built environment professionals with a comprehensive understanding of how the integration of these technologies can lead to a productive, technologically advanced industry.

As a general understating on how Data Science is seen by built environment educators, Data Science knowledge can be categorised into two main domains as: Basic to Advanced knowledge and Soft to Hard knowledge which can cover all possible approaches to explicitly incorporates Data Science into various structures of built environment curricula.

For the levels of learning outcomes to be achieved by built environment curricula, these can be defined on four levels: Awareness, Application, Competence, and Transformation. At the initial level of Awareness, the fundamental theories and use of Data Science are provided to the learners so that they know and understand Data Science. At the Application level, the learners are expected to solve close-ended built environment problems within given boundaries. These Awareness and Application levels of Data Science content can be introduced at the undergraduate level together with a broad range of built environment subject areas.

At the Competence level, learners are exposed to real-life scenarios expecting to make judgments within the actual environment. And finally, the Transformation level is intended to enable learners to make critical judgements based on sound Data Science knowledge and context and this specialisation can be achieved at the postgraduate level.

This proposed mapping of Data Science in Built Environment Education allows educators to map their programme with the intended level of cognitive complexity in Data Science content within their programme. This can be used to design learning objectives, assessments and activities that target required levels of cognitive complexity."