Multi-Disciplinary Design Activity for Undergraduate and Graduate Engineering Students
Bibliographic record
Abstract
This paper describes a project with common equipment that was adapted and offered to both an undergraduate and a graduate-level course with learning outcomes tailored specifically to each group of students. This project is an immersive, multi-disciplinary engineering design activity with a focus on materials, solid mechanics, and instrumentation. The activity incorporates aspects of fundamental engineering theory, virtual predictive simulation, as well as physical testing and data collection. All of this was done in the context of a material selection and failure analysis of a piece of furniture (cantilever chair) which is a simplistic and recognizable device by the students. The project focusses on structural analysis of the chair under a variety of loading conditions, coupled with a virtual simulation model using Finite Element Analysis (FEA). FEA is utilized to identify critical regions of the structure which are prone to failure. The complexity, constraints, and provided resources of the model varied, depending on the specific implementation of the course. Finally, a physical test apparatus was constructed and used to generate experimental responses that the students were able to use to calibrate their predictive model and theoretical hand calculations. This activity was created initially for in-person instruction but was adapted for remote delivery during the pandemic. Both qualitative and quantitative data collected from 2nd year and graduate students indicated that the activity was effective in improving several forms of knowledge acquisition. This paper will discuss in detail how a common project platform was adapted for the two academic levels with evidence of its efficacy
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".