Curricular BIM implementation plan: protocol proposal and pilot application in Brazil
Bibliographic record
Abstract
Abstract Incorporating BIM in the education of professionals has become a demand and a topic of high importance for higher education. Several proposals for a curricular implementation of BIM exist. However, in most cases, they are specific to a single university or domain, do not consider academic policies or simplify practice by segmenting knowledge. This work proposes a protocol for the curricular implementation of BIM similar to the industry, considering aspects of process, technology, and policies aligned with the educational institution's mission and with existing competencies. Action research was the used method. Three actions were conducted: (1) proposition, (2) systematization and (3) application. The actions were executed in cycles incrementally, resulting in (1) guidelines, (2) supporting tools and (3) implementation plans. Two Brazilian universities participated in pilot studies of Project Construa Brasil. The pilot studies inspired a network of BIM cells that have synchronously applied curricular BIM implementation guidelines on a large scale. The guidelines proved to be generic and comprehensive, guiding the transformation with an empirical basis for the academics' decision-making. Leadership of a BIM specialist was confirmed as an essential factor of success. It is observed that even with a timid introduction of BIM competence in a limited scope of engineering solutions, the impact on the BIM maturity curriculum is significant.
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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 | 0.112 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".