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Record W2755610874

Exploring human behaviour in design education: Supporting sustainable decision-making with a tabletop activity

2017· article· en· W2755610874 on OpenAlexaff
Amanda Willis, Alyssa Friend Wise, Alissa N. Antle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNegotiationResource (disambiguation)ScarcityKnowledge managementTheme (computing)Computer scienceManagement scienceSociologyEngineeringWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.370
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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