Current position and associated challenges of BIM education in UK higher education

In May 2011 the Cabinet Office published the Government Construction Strategy, announcing the Government’s intention to require collaborative 3D BIM (with all project and asset information, documentation and data being digital) on its projects by 2016. Together with industry the UK Government has set out on a four year programme to reduce capital expenditure and the carbon burden from the construction and operation of the built environment by 20% through a modernisation of the sector. Such proposed transformation of the construction sector has significant implications for Built Environment education providers in ensuring they meet the demands required of future professionals. To this end, this study assessed the current position and associated challenges along with perspectives of BIM education in UK HE through surveys targeted at BIM-related Academic networks across the UK. Key areas focused on included: - The definition of staff resource - State of BIM in HEI (Higher Education Institutions). - BIM adoption Strategy - BIM awareness and associated issues - Generation implications Findings from the study indicated a nuanced appraisal of BIM readiness in UK HEIs. There are clear distinctions between the category of top performers and low performers concerning investigated concepts with clear defining parameters. However the disconnect between these two tiers exists in a state of inertia that is counterproductive to the overall strategic role/contributions of HEIs to the ongoing BIM digital revolution. A BIM assessment matrix was developed from the key investigated issues. This represents an update to the optimum requirements for BIM teaching among HEIs in the UK. This update mapped additional crucial strategic considerations concerning BIM in HEIs.

[1]  D. Boud,et al.  The challenge of problem-based learning 2nd ed , 1997 .

[2]  W. Antepohl,et al.  Problem‐based learning versus lecture‐based learning in a course of basic pharmacology: a controlled, randomized study , 1999, Medical education.

[3]  S. Austin,et al.  Never waste a good crisis: a review of progress since Rethinking Construction and thoughts for our future , 2009 .

[4]  David Wheeler,et al.  Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..

[5]  Hao-Chang Lo,et al.  Utilizing Computer-mediated Communication Tools for Problem-based Learning , 2009, J. Educ. Technol. Soc..

[6]  N. Nagelkerke,et al.  A note on a general definition of the coefficient of determination , 1991 .

[7]  Construction Industry Council Model Adjudication Procedure Second Edition , 2008 .

[8]  M. Prensky Digital Natives, Digital Immigrants , 2001 .

[9]  D. Cox,et al.  Analysis of Binary Data (2nd ed.). , 1990 .

[10]  Abid Nadeem,et al.  Building information modelling for tertiary construction education in Hong Kong , 2011, J. Inf. Technol. Constr..

[11]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[12]  Jason Underwood,et al.  Embedding Building Information Modelling (BIM) within the taught curriculum : Supporting BIM implementation and adoption through the development of learning outcomes within the UK academic context for built environment programmes , 2013 .

[13]  Elisabetta Perulli,et al.  European inventory on validation of non-formal and informal learning 2014 , 2014 .

[14]  David Rindskopf Infinite Parameter Estimates in Logistic Regression: Opportunities, Not Problems , 2002 .

[15]  J. Woo BIM ( Building Information Modeling ) and Pedagogical Challenges , 2007 .

[16]  Eric R. Ziegel,et al.  Analysis of Binary Data (2nd ed.) , 1991 .

[17]  Apostolos Koutropoulos Digital Natives: Ten Years After , 2011 .

[18]  Y. Zhang,et al.  Semantic interoperability in building design: Methods and tools , 2006, Comput. Aided Des..

[19]  Dianne Gardner,et al.  Generational differences at work: introduction and overview , 2008 .

[20]  Aimi Ashikin Hanib Building Information Modelling (BIM): design process and interoperability in projects / Aimi Ashikin Hanib , 2015 .

[21]  L. Petersen,et al.  Comparing performance of multinomial logistic regression and discriminant analysis for monitoring access to care for acute myocardial infarction. , 2002, Journal of clinical epidemiology.

[22]  Rafael Sacks,et al.  Impact of three-dimensional parametric modeling of buildings on productivity in structural engineering practice , 2008 .

[23]  Jason Underwood,et al.  Delivering BIM to the UK Market , 2009 .

[24]  J. Guzmán Regression Models for Categorical Dependent Variables Using Stata , 2013 .

[25]  M. Clarke Understanding and managing employability in changing career contexts , 2008 .

[26]  Anil Sawhney,et al.  Building Information Modelling in Tertiary Construction Project Management Education: A Programme-wide Implementation Strategy , 2013 .

[27]  Nita J. Matzen,et al.  Technology as a Catalyst for Change , 2007 .

[28]  John Henderson and Andrew Croft,et al.  Government Construction Strategy 2011 - Where Are We Now? , 2013 .

[29]  Keith Martin,et al.  An innovative supported distance learning approach to motorsport industry employee education and upskilling , 2010 .

[30]  Kalle Lyytinen,et al.  Dynamics of inter‐organizational knowledge creation and information technology use across object worlds: the case of an innovative construction project , 2010 .

[31]  Charlotte H. Mason,et al.  Collinearity, power, and interpretation of multiple regression analysis. , 1991 .

[32]  S. Menard Coefficients of Determination for Multiple Logistic Regression Analysis , 2000 .

[33]  T. Watson Sociology, work, and industry , 1982 .

[34]  Peggy A. Ertmer,et al.  Examining Teachers’ Beliefs About the Role of Technology in the Elementary Classroom , 1999 .

[35]  David Theo Goldberg,et al.  The Future of Thinking: Learning Institutions in a Digital Age , 2010 .