BUILDING THE BIG PICTURE: A COMPARISON OF FIRST YEAR DESIGN COURSES IN ENGINEERING AND INDUSTRIAL DESIGN
Bibliographic record
Abstract
For the student entering engineering and associated design degree programs the challenge to master the range of fundamental knowledge skills is considerable in itself. In most disciplines the knowledge base on which student courses are constructed is both changing and growing at an increasing rate. As a result it is now difficult for any student to see how these technical, scientific and mathematical courses fit into the big picture of the discipline they plan to become part of. Their high school experience is limited to general awareness at best, but is largely focused on fundamental sciences. This is obvious in engineering, but is possibly more problematic for a student interested in industrial design, with the same core mathematics and physics requirements, yet often without the appropriate visual and creative courses. Additionally, first year students come from a range of backgrounds, which makes the initial courses relating core material to their chosen professions more difficult to focus. This paper details the work of two introductory first year courses – An Engineering course from the University of Manitoba is compared and contrasted with a Design Studio Course in the School of Industrial Design at Carleton University. In both courses the range of projects given may at first glance seem quite simple, but each has been designed and developed to build the bridge of understanding between fundamental skills and the profession it leads to. Both courses try to take full advantage of the opportunities the projects present within the limited time available. Success in such courses is usually achieved by ensuring that every project/challenge is not seen as ‘the design of a new something’, but that it is couched in an understandable reality. This might be achieved by the design process the students are expected to go through, the context in which the design challenge is set, the history and evolution that brought the project to this particular stage, the significant economic, social and business drivers, and of course the fundamental skills and knowledge base being developed. The aim of this paper is to help identify a pattern that other first year engineering/design courses could adopt or modify, as the challenge of ‘Building the Big Picture’ become more essential.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".