Workplace-Based Learning: A Study in BIM-enabled Construction Projects

Gangatheepan, Sivagayinee (2023) Workplace-Based Learning: A Study in BIM-enabled Construction Projects. Doctoral thesis, Birmingham City University.

Sivagayinee Gangatheepan PhD Thesis published_Final version_Submitted Nov 2022_Final Award Jan 2023.pdf - Accepted Version

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Building Information Modelling (BIM) is a fast-emerging technology that has promoted digital transformation in the construction project lifecycle through changing the ways in which people work. However, empirical studies show that professionals in the construction industry are still reluctant to adopt BIM in their construction projects due to a lack of skills and suitable learning approaches. Furthermore, embracing an appropriate learning approach is still challenging in built environment projects, which are generally complex, temporary, unique and uncertain due to their fragmented nature. To achieve more successful BIM-enabled construction projects, a flexible and relevant learning approach for the workplace needs to be determined. Consequently, resolving this issue requires identification of the key learning aspects that influence creation of a suitable learning approach. The aim of this doctoral study is to explore how workplace-based learning could be designed and implemented in BIM enabled-construction projects.

Learning that takes place in construction projects is predominantly determined by complex social practices. On the other hand, BIM – which professionals desire to adopt in construction projects – is interwoven with both interactions with humans and artefacts. To holistically investigate the learning in BIM-enabled construction projects, ‘Connectivism’, a new learning approach for the digital age, is adopted in this study. This explains the complex learning that happens in the work environment through a combination of principles by understanding the unrelated unseen events (chaos), exploring the learning as a collective (network), investigating the position between order and disorder (complexity) and analysing unpredictable and uncontrollable learning that occurs due to non-linear interactions (self-organising). Understanding the continuous learning in both human and non-human activities through Connectivism has helped to identify the links between the key learning aspects in the workplace. Examining the identified learning aspects in a connected way has encouraged professionals to figure out the most suitable learning approach for their project team.

This study has been conducted in three phases: literature review, semi-structured interviews and a case study approach, in order to understand the learning that occurs in BIM-enabled construction projects. Semi-structured interviews were conducted with 20 professionals working in BIM-enabled construction projects. Two case studies were selected to analyse BIM-enabled construction projects in the £30-60 million scale. Furthermore, six case studies within those selected projects were chosen for an in-depth investigation on the in-project learning. Data within the case studies were collected through project documents, semi-structured interviews and meeting observations. Nvivo was used to evaluate, interpret, explain and analyse the data collected from both semi-structured interviews and case studies.

The study reveals that BIM-enabled construction projects are largely involved with information that is digitally linked with federated 3D models and project participants. Investigation shows that learning in in these projects is continuous, networked and depends on participation in addition to knowledge accumulation and knowledge creation. ‘Participation’ and ‘Interpretation’ as a combination have significant impacts on this complex learning that takes place in work environments. ‘Participation’ at work shows how each individual wants to get involved, interpret and learn in each situation that they participate. On the other hand, the multidisciplinary nature of BIM-enabled construction projects confirms that project participants need to focus on interpretation to agree on a common meaning of artefacts and information. Therefore, ‘Interpretation’ is identified as a form of thinking that comprises planning, monitoring one’s activities and problem-solving. Interpretation, which is enabled via thinking and sharing experience, helps to shape the decisions and solutions during Participation. To help construction projects in achieving a suitable learning approach which is vital for a success of a project, a model for learning in the workplace has been developed through merging the learning aspects that have been identified from chosen BIM-enabled construction projects.

The novel model for workplace-based learning is a combination of participation and interpretation which is linked through three learning modes: Alignment, Insight and Engagement. The combination of these learning modes has contributed to interpret the ideas while participating at work. Consequently, it enabled project participants to align on a common meaning in an informative collaborative environment. The proposed model of learning in the workplace presents a systematic approach for achieving suitable learning in BIM-enabled projects by connecting the key learning aspects at the project level. Furthermore, this can be also used to employ skilled people and promote common standards on skills expectations associated with BIM-enabled projects.

Item Type: Thesis (Doctoral)
14 November 2022Submitted
30 January 2023Accepted
Uncontrolled Keywords: Workplace-Based Learning, Building Information Modelling (BIM), Connectivism
Subjects: CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
CAH13 - architecture, building and planning > CAH13-01 - architecture, building and planning > CAH13-01-02 - building
Divisions: Doctoral Research College > Doctoral Theses Collection
Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Jaycie Carter
Date Deposited: 18 Apr 2023 12:12
Last Modified: 18 Apr 2023 12:12

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