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Record W2014127578 · doi:10.1061/41109(373)21

Interdisciplinary Team Learning in the Context of Integrated Design Studio

2010· article· en· W2014127578 on OpenAlexaff
Ivanka Iordanova, Daniel Forgues, Michael Jemtrud, Leila Marie Farah, Temy Tidafi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsÉcole de Technologie SupérieureUniversité de MontréalMcGill University
Fundersnot available
KeywordsDesign studioMultidisciplinary approachStudioKnowledge managementComputer scienceContext (archaeology)Integrated designSustainabilityDesign educationProcess (computing)EngineeringEngineering managementProcess managementSociologyBusiness

Abstract

fetched live from OpenAlex

Scientific literature and practical experience point out fragmentation of the building design process as one of the reasons for ineffective design and construction processes. This paper proposes interdisciplinary team learning in the context of an integrated design studio as a successful approach for both: achieving sustainable design solutions and transforming disciplinary cultures into integrated practices. In a rather unique collaborative experience based on strategies from situated learning and activity theory, students with different design and construction backgrounds were placed in multidisciplinary (co-located) teams to work on a real architectural project with specific requirements for sustainability. This integrated design studio was timed in three intensive 2-day charrettes. A coherent digital environment for integrated design was proposed in order to meet the needs of the multidisciplinary teams. The results from this experience were very positive in respect to both individual and team learning.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.356

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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 designQualitative
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

Citations6
Published2010
Admission routes1
Has abstractyes

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