WHICH TYPE OF DESIGN PROJECT IS BEST: NARROW AND DETAILED OR BROAD AND CONCEPTUAL?
Bibliographic record
Abstract
Engineering design is an essential part of the engineering curriculum, and it is important to select projects with appropriate scope and challenge to develop the desired skills subject to constraints on time, student ability and available resources. This paper considers two types of projects typically encountered in capstone design: detailed design projects, and conceptual design projects. Detailed design projects usually have a goal of constructing and testing a physical prototype, and the main focus is on CAD modeling, detailed analysis, engineering drawings, manufacturing processes, prototype fabrication, and testing. Conceptual design projects focus on the conceptual design stages, and typically do not result in a prototype. These projects are more open-ended, and focus on problem definition, background research, order-of-magnitude analysis, numerical simulation, technical and economic feasibility, and consideration of nontechnical aspects including impact on society and the environment. This paper compares and contrasts the two types of projects in terms of their characteristics, and evaluates them based on the CEAB Graduate Attributes. Both types of projects provide valuable and complementary design experience, and each type emphasizes different attributes.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".