Exploring human behaviour in design education: Supporting sustainable decision-making with a tabletop activity
Bibliographic record
Abstract
This paper explores the behaviour of learners engaging with a sustainable tabletop activity. Fitting with the theme of Resource-Sensitive Design, this paper takes the viewpoint that the early educational experiences of future designers can shape how they conceive the complex issues of resource scarcity, and therefore design education using technology can support learning and behaviours for sustainable decisions. Videos of twenty pairs of students playing the land planning game “[Blinded]” were qualitatively analyzed using a speech-act theory framework to identify emergent themes on collaboration and decision-making. The findings showed that learners used tools with speech acts in many ways that enhanced collaborative behaviours: 1. advocating for issues using evidence, and 2. sharing values to convince a partner and 3. engaging a non-attentive partner. The implications for design include supporting: informed decision-making, highly visible information, buy-in processes, and encouraging learners to express their values. These findings provide new avenues for exploring spaces for negotiation about the environment and decision-making about difficult trade-offs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".