Bibliographic record
Abstract
At its core, engineering technical design is the process of taking a concept to creation. In order to achievesuccessful technical design a student must combine their idea or vision of a solution (function) with a visualization of thepart/assembly (form) -- referred to henceforth as vision-ization. Teaching technical design to a large first-year class ofengineering students presents a number of challenges, but perhaps the most significant is the rapid change of the toolsused in engineering technical design. To be clear, the tools themselves are not the challenge, as the students generallyhave no trouble mastering the tools. The challenge lies in the teaching and ultimately the learning objectives; at aUniversity level the fundamental question is what pedagogical benefit does a tool provide without the knowledge to applyit? As the tools have advanced, the students (and the instructors) find themselves further from the design process resultingin course topics perceived as disconnected or without relevance. In 2006 McMaster University's first-year engineeringprogram departed from the traditional method of teaching engineering design, which was heavily focussed on form, toestablish design function as the primary objective of the course. With a yearly enrolment near 1000 students, thescalability of teaching and evaluating design function was implemented using a customized simulation and visualizationtool. The simulation was extended to the logical use of rapid prototyping machines (3D-printers) for physical creation andtesting. This paper will present the author's initial analysis of the link between pure visualization, applied visualization,and success in functional design via rapid prototyping..
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".