Arts education in Hong Kong kindergartens: provision of activities and impact of teachers’ demographics
Bibliographic record
Abstract
Arts and Creativity is one learning area of Hong Kong’s official kindergarten curriculum framework. Kindergarten teachers are expected to foster children’s creativity via four art forms: music, visual arts, dance, and drama. However, prior studies have investigated the provision of activities for each of the art forms in isolation and have not explored the demographic variables that predict teachers’ provision of arts education activities. Investigating the provision of the four art forms and its relationship with teachers’ demographics could provide an overview of the status of arts education in Hong Kong kindergartens. We had two research goals: (1) investigate the frequency with which kindergarten teachers conduct arts education activities focusing on music, visual arts, dance, and drama; (2) identify subgroups of teachers who differ regarding the provision of arts education activities and analyze how key demographic variables predict their memberships to these subgroups. We surveyed 477 teachers. Descriptive statistics, latent profile analysis, and multinomial logistic regression analysis were performed. We found that the presence of the four art forms was unbalanced. Participants reported conducting music and visual arts activities frequently, while dance and drama activities were occasionally or rarely conducted. Moreover, we identified three subgroups of teachers who provided arts education activities with different frequencies. Participants with a master’s degree and those who worked in government-funded kindergartens were more likely to be in the highest arts provision group. Findings suggest that the curriculum is not being implemented accurately, as teachers do not equally expose children to the four art forms. We interpret these findings as a reflection of teachers’ uneven preparation in the various art forms. Implications for educational policy regarding professional development are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".