Bibliographic record
Abstract
Art education plays a crucial role in fostering creativity, critical thinking, and cultural appreciation among students. Central to the effectiveness of art education is the role of fine art teachers who serve as mentors, guides, and facilitators in the learning process. This abstract explores the perceptions of art education and students' perceptions of their fine art teachers, shedding light on the impact of teacher-student dynamics on the overall educational experience. The study employs a mixed-methods approach, combining surveys and interviews to gather data from a diverse sample of students enrolled in various art education in fine art programs [i](Smith et al., 2022; Brown & Jones, 2021) suggest that positive teacher-student relationships in art education contribute to increased student engagement, motivation, and a deeper understanding of artistic concepts. Furthermore, the research delves into the role of fine art teachers in nurturing students' individual artistic voices and fostering a sense of community within the classroom. Insights from interviews reveal students' perspectives on the qualities that make an art teacher effective, such as approachability, passion for the subject, and the ability to provide constructive feedback. This abstract concludes by highlighting the implications of the findings for both educators and policymakers in shaping art education curricula and professional development programs. By understanding the dynamics between art education and students' perceptions of fine art teachers, stakeholders can work towards creating a more enriching and supportive learning environment for aspiring artists.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.972 | 0.963 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".