Threshold concepts in art education: Negotiating the ambiguity in pre-service teacher identity formation
Bibliographic record
Abstract
Abstract The dual nature of being simultaneously an artist and a teacher has long been recognized as a source of stress for novice and mid-career art educators. The article describes findings from a qualitative study into first-year art education students’ experiences of studio-based art courses. Data comprising interviews with six instructors and focus-group interviews with a total of twelve students revealed the extent to which the above problem takes root early on before prospective teachers have entered the classroom, and indicates a pressing need for pre-service art teacher training to address this issue. We suggest the inherent dichotomy at the heart of art education can be understood as a problematic liminal space, which students experience both on a personal as well as an institutional level, and we explore how Threshold Concept theory might serve as a key to help students fuse their conflicting identities into a coherent sense of self.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".