ENHANCING DESIGN EDUCATION BY PRODUCT REVERSE ENGINEERING
Bibliographic record
Abstract
As science and technology develops faster and faster, the accumulation of knowledge is exponential over time. Engineering education must keep up with the changing environment including engineering practice. As each individual’s capability is limited, engineering students need choosing right stuff to learn so that they can graduate as qualified engineers with both broad knowledge and practical skills as required in industry. In this paper, the current engineering education is discussed with some trends, such as creativity training as most have insisted in project-based hands-on design education, broad knowledge including essential engineering science knowledge. As a comprehensive discipline, design engineering courses exist to teach engineering design fundamental. Due to immature design theory and methodology, the “learning by doing” approach is widely accepted to complement current engineering design education. In this paper, an integrated effort is introduced which combines together the two basic aspects, knowledge and skill, in order to increase the half-life of engineering knowledge and enhance the hands-on skills at the same time. Based on new development in design research, an experimental design education using Product Reverse Engineering (PRE) as education tool, is introduced with initial evaluation for suitability in design education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".