Learning to Be: The Modelling of Art and Design Practice in University Art and Design Teaching
Bibliographic record
Abstract
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".