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Record W2469359618 · doi:10.1111/jade.12060

Learning to Be: The Modelling of Art and Design Practice in University Art and Design Teaching

2016· article· en· W2469359618 on OpenAlexfundno aff
Kylie Budge

Bibliographic record

VenueInternational Journal of Art & Design Education · 2016
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
FundersVictoria UniversityUniversity of MelbourneUniversity of Victoria
KeywordsArt methodologyVisual arts educationTacit knowledgeArt designDesign educationSociologyArt and designProcess (computing)Professional developmentPedagogyVisual artsEngineering ethicsContemporary artEngineeringArtComputer scienceKnowledge managementThe arts

Abstract

fetched live from OpenAlex

Abstract Learning to be an artist or designer is a complex process of becoming. Much of the early phase of ‘learning to be’ occurs during the time emerging artists and designers are students in university art/design programmes, both undergraduate and postgraduate. Recent research reveals that a critical role in assisting students in their maturing identities as artists and designers is played by artist/designer‐academics teaching in university art and design programmes. By maintaining active art/design practices and drawing from these in their teaching, artist/designer‐academics model professional practice to students. Witnessing and interacting with such modelling is part of the process of students learning the shared discourses, views and practices of the art or design worlds to which they aspire to belong. The modelling of professional practice is critical to an artist or designer's ‘learning to be’ experience because it enables students to access the tacit and nuanced behaviours, languages and cultures that constitute contemporary art or design practice. This article outlines findings from a recent Australian study revealing the role of professional practice modelling in university art/design teaching. It highlights the centrality of professional practice modelling to artist/designer‐academics in their beliefs and approaches to teaching their academic disciplines. In critically exploring the research data and findings this article describes the role that modelling of practice plays and how it comprises a core part of the value that artist/designer‐academic participants contribute to the teaching of art/design education.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.078
GPT teacher head0.368
Teacher spread0.291 · 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 designNot applicable
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

Citations21
Published2016
Admission routes1
Has abstractyes

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