ENGINEERING SKETCHING AS A VISUALIZATION TOOL - PART DEUX: VISUALIZING ENGINEERING CONCEPTS
Bibliographic record
Abstract
Engineering sketching is a method of externalizing the thinking about, and solving of, design problems. Sketching, from the Greek σχεδιος (meaning “sudden”), can be defined as: 1. A simple, rough drawing or design; 2. A brief plan or description of major elements. Sketching exists somewhere between writing and formal drawing as a means of formulating ideas. In the third year of teaching engineering sketching in our first year design course, assignments were given an additional component: the visualization of engineering concepts. This had three motivations: 1. Students should be given the opportunity to integrate knowledge from other first year engineering courses; 2. Students should be challenged to think spatially; 3. Students who were not necessarily strong renderers should be able to do well in the “concept” category. This paper will discuss how these new components encouraged the students into a more Visual-Spatial (VS) thinking mode. There will be some discussion regarding how VS type students “understand” with respect to more prevalent Auditory-Sequential (ASQ) type students. The goal of visualizing engineering concepts is to bridge the ASQ style of deliveries (from other knowledge-bases) with a more VS style of problem solving.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".