Cornerstone Design: Product Dissection In A Common First Year Engineering Design And Graphics Course
Bibliographic record
Abstract
In the senior year of an engineering program many students will have the opportunity to enroll in courses that offer Capstone engineering design projects [1].In many engineering students' educational career these are the most interesting and rewarding courses because they offer the student the ability to apply the culmination of their education to an engineering design problem.This is often described favourably by the student as their first "engineering" experience and in general it provides a greater appreciation for the field of engineering and a motivation for greater knowledge.If this type of experience could be offered to first year students it would significantly enhance their engineering education.However, the challenge for a first year engineering program is balancing the required background knowledge for design against a procedure for demonstration; this is an even greater challenge for a common curriculum.Just as the Capstone represents the tip of an engineer's education, we offer the Cornerstone Design to represent the base [1-2].The objective of the Cornerstone is to instill in first year engineers enjoyment from learning, motivation to continue learning, and genuine intellectual curiosity about the engineering in the world around them.This paper will present our work in structuring and delivering the Cornerstone Design Project as a product dissection and modeling to 1000 first year engineering students in a Design and Graphics course.The paper will also report on student feedback regarding the project and its effect on their motivation and engagement to the course material.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".