TEACHING CIVIL ENGINEERING DESIGN USING PROJECT-ORIENTED INDUSTRY DRIVEN CAPSTONE COURSES
Bibliographic record
Abstract
Teaching civil engineering design through senior projects or capstone design courses, with industry involvement and support, has increased in recent years. The general trend toward increasing the design component in engineering curricula is part of an effort to better prepare graduates for engineering practice. While some design projects are still of the “made up” type carried out by individual students, the vast majority of projects today deal with “real-world problems” and are usually conducted by student teams. The paper begins first by briefly reviewing the design as a “thought” process, focusing on several dimensions of “design thinking” and how “design thinking” skills are acquired. Second, the paper reports on the development, implementation, and subsequent evaluation of a senior design course at an international university, where practitioners have played a major role in planning and teaching the capstone course. The new, restructured design course, co-taught by practitioners from the Region, has met its declared objectives and exposed students to professional practice. This industry-driven experience has also provided information with regard to curricular content and capabilities of departmental graduates. In a way, the capstone experience reported on in this paper, serves as a microcosm of the four year program. Experiences and outputs from the course can be used to provide guidance and insights into curricular changes, teaching methods, and exposure to civil engineering practice in the Region; and helps in establishing enduring connections with the industrial sector.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".