Data-Driven Design as a Vehicle for BIM and Sustainability Education
Bibliographic record
Abstract
The development of BIM pedagogical strategies within the Architecture, Engineering, and Construction disciplines is a topic of significant research. Several approaches and theoretical lenses, such as Project-Based Learning, constructivist pedagogy, experiential learning, and Bloom’s Taxonomy have been applied to guide pedagogical education. This paper presents the development and evaluation of an approach integrating these four perspectives that was developed within an Architectural Science undergraduate program. A data-driven design project was incorporated into the curriculum to give students opportunities to engage with BIM-based simulation (cost and energy) to guide their design studio project development. The pedagogical approach is discussed, along with refinements to this project based on early implementation. Four years of data are analyzed, consisting of 1325 design iterations and student feedback on the project. A critical evaluation of the project determined that it was highly effective to engage students at an advanced level - level 4 (Analyze) of Bloom’s Taxonomy was consistently achieved (over 96% of students) and two thirds of students also engaged meaningfully at Level 5 (Evaluate; 67%) and/or 6 (Create; 8%) — while developing a high degree of competence in the use of BIM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".