Enhancing BIM competencies of built environment undergraduates students using a problem-based learning and network analysis approach

Abstract

Purpose

Building information modelling (BIM) is an innovative, collaborative process underpinned by digital technologies introduced to improve project performance in the architecture, engineering and construction (AEC). Growth in industry demands has necessitated BIM inclusion into the higher education (HE) curricula as both a pedagogic and practical objective to prepare and develop aspiring built environment (BE) professionals with the required competence for contemporary practice. However, comprehension of BIM concepts and subsequent development of the skill set required for its application remains overwhelming for students. In mitigating this challenge, adopting appropriate learner-centred strategies has been advocated. Problem-based learning (PBL) is becoming a widespread strategy to address concerns associated with authentic practices.

Design/methodology/approach

This paper evaluates the impact of the PBL strategy on students' accelerated learning of BIM based on a case study of 53 undergraduate students in a BIM module. The network analysis and centrality measures were employed in understudying the most applicable BIM skills.

Findings

From the analyses, PBL benefits students' knowledge acquisition (cognitive and affective) of BIM concept and development of transferable skills (academic and disciplinary), equipping them with capabilities to become BIM competent and workplace ready for the AEC industry.

Originality/value

The BIM pedagogy evolves, and new skillsets emerge. Analytical, communications and collaboration skills remain sacrosanct to delivering BIM modules. These skills mentioned above are essential in getting undergraduate students ready to apply BIM in the AEC sector.

Citation

Obi, L.I., Omotayo, T., Ekundayo, D. and Oyetunji, A.K. (2024), "Enhancing BIM competencies of built environment undergraduates students using a problem-based learning and network analysis approach", Smart and Sustainable Built Environment, Vol. 13 No. 1, pp. 217-238. https://doi.org/10.1108/SASBE-05-2022-0085