Developing the Skill of Problem Definition Through Model Creation and Exploration
Bibliographic record
Abstract
Problem definition is an extremely important part of the design process and yet there are few tools widely known and available to help novices and experts in the definition/diagnosis process [1-3]. The authors hypothesize in this pilot study that if novices gain experience “playing” with prototypical problems from common categories they will be better at identifying said categories in ambiguous contexts. Students were introduced to several classic model types in Industrial Engineering (IE) and given short numerical open-ended weekly assignments to explore a model’s assumptions, limitations, and ramifications. Students were then given realistic case studies and asked to diagnose them as an example of a prototypical problem as well as related questions and were scored accordingly. Students performed approximately 10% worse in their problem-definition task when they did not have experience engaging in an exploratory modelling assignment. The results of this pilot study suggest that the ability to diagnose problems well increases when students have engaged previously in numerical modelling tasks. It is therefore tentatively concluded that a key aspect of design education should include experiential knowledge and practice with key problem categories to enable expert-like breadth-first search instead of using a typical novice-like depth-first search.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".