Continuing Education and Personalization of Design Methods to Improve their Acceptance in Practice – An Explorative Study
Bibliographic record
Abstract
One possibility to establish and foster efficient method transfer from academia to industry is via the heads of professional designers and design students. The transfer and use of design methods in a sustainable way is related to the methods’ acceptance by the user which is accompanied by many challenges. Educational concepts and design method adaptions have been chosen as decisive control parameters among many others in order to understand and evaluate how these can influence the acceptance of design methods in industry. An interview study to gain an understanding of the rationale of educational needs of engineers has been conducted to enrich existing literature in this area. The evaluation of feedback from academia-industry cooperation revealed specific challenges accompanied with educational concepts for modularization design methods. Based on these findings, an adaption was developed and an experiment study was conducted with students as future designers to decode variable factors in design training, gain qualitative feedback to a specific adaption and gain an understanding for the conditions and limitations of an experimental study. Joint conclusion reveals the need for improved education in method transfer and adaptions of the design methods to user-specific needs and paved the way for a series of experiments with various treatments of study participants regarding different personalized adaptions in design methods.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".