COGNITION AND TRANSDISCIPLINARY DESIGN: AN EDUCATIONAL FRAMEWORK FOR UNDERGRADUATE ENGINEERING DESIGN CURRICULUM DEVELOPMENT
Bibliographic record
Abstract
Abstract - Contemporary engineering product design and development no longer adheres to the boundaries of a single discipline and has become tightly integrated, often relying on interaction of multiple disciplines for completion of integrated product design projects. In order to design these products, design and development practice has transcended the discipline boundaries to become a transdisciplinary engineering design process. A collaboration of specialists from different engineering disciplines is required to develop efficient solutions to interdisciplinary problems of product design. Despite this shift from mono-disciplinary to transdisciplinary, the engineering design curriculum remains focused on teaching discipline specific design practice through skill based subject specific pedagogy with a limited emphasis on the importance of design process and transdisciplinarity in the design process. As a result, new graduates starting in design and development organizations face a difficulty finding a common basis of understanding of disciplines’ interactions and must go through a process of often implicit ‘onboarding’ to understand the transdisciplinary engineering design process. This can be avoided by developing and adapting undergraduate design process education in line with industrial demands. This paper proposes a theoretical framework based on empirical engineering design research in industry, educational psychology and teaching approaches such as Bloom’s Taxonomy and Kolb’s Model of Experiential Learning for developing the core elements of a transdisciplinary engineering design process curriculum.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".