Global Multidisciplinary Learning in Construction Education: Lessons from Virtual Collaboration of Building Design Teams
Bibliographic record
Abstract
Construction is a multidisciplinary activity in which effective communication between parties is essential for successful construction projects. However, the construction industry has been characterised by fragmentation, which prevents seamless communication. This problem has been further exacerbated by the need to communicate over distance within a time constraint in an increasingly interconnected and globalised construction sector. This has brought a particular challenge to the education sector in preparing the future graduates to work in this context. The paper reports on an on-going Hewlett Packard-sponsored project to implement an innovative learning approach which consists of distanced collaboration between students from different disciplines from two Universities in the UK and Canada. The empirical work involved interviews and questionnaire survey at different stages of the project. The preliminary findings reveal the impact of disciplinary training on the development of effective virtual collaboration, although this has been moderated, to some extent, by their earlier (not so positive) experience during the course of the project. The research provides a material for further reflection and may serve as a useful consideration for future development of a guiding framework for effective training of built environment professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".