A framework for building information modeling implementation in engineering education
Bibliographic record
Abstract
Universities are facing many challenges to their efforts to introduce building information modeling (BIM) in engineering education. Many research efforts have been dedicated to the subject and addressed some specific aspects of the issue. Thus, there is no comprehensive framework to provide decision-makers with practical and neutral guidelines. The framework proposed in this paper identifies the main challenges to address. A case study from a Canadian engineering school is used to evaluate and to validate the proposed framework, and to illustrate the challenges. The strategy of integrating BIM in engineering education should be based on the specific skills the students are expected to acquire. It is then possible to define the appropriate teaching approaches. An effective implementation strategy should be gradual to progressively raise community awareness, learn from mistakes, and identify best practices. A particular emphasis should be placed on the needs of the local industry.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".