Offsite Construction education adoption in Civil Engineering undergraduate curriculum: analysis and proposal
Bibliographic record
Abstract
Offsite construction (OSC) is not new globally yet is still not widespread in Civil Engineering (CE) programs. This study proposes strategies for implementing OSC education in a CE undergraduate program curriculum. The research method used was the exploratory field study. Data were collected from three online questionnaires sent to interested parties: OSC industry professionals, faculty members, and final-year students. Each questionnaire sought to answer a specific objective of this study: to identify the design and onsite assembly competencies demanded by the industry, identify the interfaces between OSC and the CE program curriculum and determine the level of confidence of final-year students in applying OSC competencies. The collected data analysis was qualitative and generated from a crossing of the data from the three questionnaires, which supported the proposition of hypotheses for the insertion of OSC teaching by identifying the needs, deficiencies, and difficulties the three interested parties presented. Findings suggest that the OSC competencies most demanded by the industry are about knowing how to detail the interfaces between the components and the parts of the construction site, guarantee the assembly of elements within the deadlines, or learning how to take safety measures against accidents during the construction site. Data also suggest that students are interested in the subject but graduate with little confidence in applying most of the design and onsite assembly competencies demanded by the industry. One of the few exceptions is the knowledge to take safety measures against accidents on the job site. As a result, two hypotheses were generated to adopt OSC teaching in a Civil Engineering program.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".