The Implementation of a Problem Formulation Project with a Sustainability Focus
Bibliographic record
Abstract
The problem formulation stage of engineering design is an important aspect, however little time is spent teaching our students how to do this or providing time to practice. Students need to practice expert-like skills and behaviours involved in researching and understanding ill-defined problems, as opposed to textbook problems with clear solution paths. This paper describes the implementation of a project that focuses on the tasks of researching and understanding a problem in sustainability using known campus sustainability problems from the University’s sustainability office. Three current opportunities were selected as problem domains based on their open-ended nature and ease of understanding at a basic level. To evaluate the activity, a survey was given to students before taking part in the activity, and again at the completion to assess whether there were any changes after completing the project. The instructor guided approach allowed the students to practice the problem formulation stage of the engineering design process in a supportive environment. The use of open-ended ill-defined relevant campus sustainability problems provided an opportunity to interview stakeholders. It was found that the structure of the project was successful in meeting the intended activity outcomes.
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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 | 0.049 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".