The Analysis of three Case Studies as a guide to the Development of Pedagogy in Engineering Design Education
Bibliographic record
Abstract
The Case Study has become a pedagogical vehicle ofchoice in helping engineering students to gain perspective on the multidisciplinary realities of design. What once were termed ‘war stories’ have evolved to a level where case studies are available and downloadable on all manner of topics. For the fundamental knowledge-based issues of engineering, example questions have commonly been created to help the student manoeuvre through all manner of possible combinations of application. The case study is not however fabricated, and relies on the reporting and documentation of a real design or engineering product development. In recent years many of these case studies have been related to ethics and communication, but very few have been related to ongoing product development and issues of Industrial Design. This is not surprising since the creation of such case studies is time consuming, and design is often a ‘messy’ process in which few companies would be keen to expose their failures along the way. Nevertheless case studies are a vital part of Engineering Design education and offer excellent potential for the development of the pedagogy vital to the dynamic formulation of Engineering Design Education. This paper references three design projects undertaken professionally by the author as an Industrial Designer working with predominantly engineering based companies. The aim is to identify critical aspects of these projects that could be used as lessons, perhaps, but not necessarily, as case studies, but to be incorporated into engineering design education.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".