Options to Bring Design and Construction Experience into the Classroom
Bibliographic record
Abstract
The construction industry is experiencing an array of significant, rapid, and revolutionary changes driven by forces such as globalization, advances in engineering technologies and information systems, sustainability requirements, and the multidisciplinary and complex nature of the problems in the 21st century. These changes require the higher education system to follow and prepare the next generation of professionals for success in the variety of professions supporting the life cycle of the project. Integrating design and construction experience into the typical academic curricula is at the core of this requirement, which gives students the required vision to understand and appreciate the industry, its challenges, and its needs for innovation. This paper presents a number of practical options to bring design and construction experience into the undergraduate level construction engineering and management programs. Some of the options presented in this paper have been widely practiced in major universities or colleges. This study aims at reviewing and summarizing these practices and presenting them through separate guidelines for construction faculty, students, and practitioners. Although the major focus of this paper is on undergraduate education, the presented guidelines can also be adopted for graduate programs. A case study of a successful student engagement in industry practice is also presented, and additional remarks and suggestions are discussed.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".