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Record W3214077776 · doi:10.24377/dteij.article1343

Integrated studio approach to motivate collaboration in design projects

2023· article· en· W3214077776 on OpenAlexaff
Virginie Tessier

Bibliographic record

VenueLiverpool John Moores University · 2023
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStudioDesign studioArchitectural engineeringEngineeringEngineering managementSystems engineeringComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In an attempt to resolve some of the gaps associated with the pedagogical integration of teamwork in design curricula, this article seeks to share a model for learning teamwork skills. This model is the result of a multiple case study methodology based on the learning experiences of 22 design students. Data was collected during various team projects through questionnaires and interviews. In relation to the concept of the zone of proximal development, the coded data was organised by thematic categories and training levels to provide a practical tool to support teaching and assessment practices to encourage the learning of teamwork skills. The proposed model allows for a systemic understanding of teamwork skills that should be acquired during design training to navigate with efficiency and confidence in the collective projects of design’s community of practice. The use of the model promotes the adoption of more complex teamwork dynamics, such as collaboration, enhanced with an integrated pedagogical approach. It also motivates individual action towards collaborative initiatives in the hopes of more coherent teamwork processes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.035
GPT teacher head0.239
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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