Beliefs and practices of visual arts and aesthetics by non-specialist primary art teachers
Bibliographic record
Abstract
This study explores how non-specialist primary art school teachers' beliefs and practices emerge in their teaching of visual art and aesthetics in the classrooms. A literature review suggests that very little research exists in this area. In order to contextualize how teaching may be influenced by art and aesthetic knowledge views are presented of aesthetic philosophy literature, as well as the theoretical models of Walker and Parsons, and the curriculum orientations perspectives of Eisner and Vallance. The three data sources are lenses that interact to inform the interpretations of the problem. Theory triangulation applies to the interpretation of the case studies that are contextualized by the literature review and the interviews with the arts educators. Three cross-case analyses are created to interpret the case studies of the non-specialist primary art teachers: personal experiences and self-images in art, beliefs in art, and practices in teaching art and aesthetics. There are contrasts and similarities among their beliefs and practices of art. The interactions among personal experience, curriculum, and practice are explored throughout the interpretations. School culture emerges as important in shaping their collaboration and teaching practices in art. Very few of the teachers had an awareness of literature that might inform their practices of teaching art. Experiences in their teacher education was a factor in determining their motivation for the teaching of art. It also provides suggestions for further research to improve non-specialist primary art teachers' practices based on the exploration of the non-specialist primary art teachers' art experience and how it influences their teaching. Several different sources of data and methods are used as lenses to inform the problem. The three procedures are: (1) A review of government educational documents: The Formative Years (1975), The Common Curriculum (Revision, 1995) and the predominance of the Ontario Curriculum (1998). (2) Interviews with three arts educators. (3) Case-study analyses using interview and observational data of four non-specialist primary art teachers. This study has implications for changes in teacher education programs because most teachers do not have art backgrounds.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".