A New Graduate Course on Modular Construction: University of Nevada, Las Vegas
Bibliographic record
Abstract
Modular construction has been highlighted as one of the key technologies which can significantly improve the construction industry by major professional conferences (i.e., 2017 CII (Construction Industry Institute) Annual Conference, Autodesk University (Las Vegas 2017), CONEXP- CON/AGG) held in 2017. It is now evident that practitioners in the construction industry recognize and pay more attention to the value of modular construction, and consider implementing it. One of the enablers that can accelerate higher levels of modularization across the industry is changing project stakeholdersäó» stick-build paradigm to modularization. However, as most of the engineering schools in the U.S. teach courses based on the stick-build approach, students do not have an opportunity to learn the modular approach. Due to this reason, when they become owners, designer, and contractors, they are captured by the stick-build paradigm and more likely become reluctant to expand their modularization äóěcomfort zones.äóť To accelerate higher levels of modularization and meet the need of students and the industry, the Department of Civil and Environmental Engineering and Construction at the University of Nevada, Las Vegas, led by Dr. Jin Ouk Choi, recently created a new graduate-level course on Modular Construction in 2017 which covers an overall understanding of modular construction concepts including, advantages, disadvantages, impediments, industry status, business case process, execution plans, critical success factors, and standardization strategies of modularization. This paper will introduce the course in terms of its vision, learning objectives, development procedure, structure, contents, and studentsäó» feedback who took the course in Spring 2017.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.018 |
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".