Developing a Cornerstone "Human in the System" Engineering Design Course
Bibliographic record
Abstract
The authors describe their experiences creating a cornerstone engineering design course for mechanical and industrial engineering undergraduate students. Starting with a tabula rasa, we have been working to create a one-semester design experience that integrates Human Factors (HF) directly into every aspect of engineering design. In the last decade, we have identified three key issues with which we grapple: lack of integration of HF in design; lack of access to cohesive HF data; and dysfunctional student teams. Given the lack of available information upon which to draw for the design of this course, we adopted a CQI-like iterative, organic, and evolutionary approach. In this paper, we present many of the ways we have attempted to address these issues, relating to courseware development, course management, assessment and grading, and student and instructor support. We summarize by presenting our advice to others who are looking to fully embed HF or other non-design fields into a cogent design experience for their students. All our courseware and tools are available freely on the web.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".