Analyzing the Applicability of Preschool Curriculum Using Children’s Songs from the Perspective of Preschool Teachers: An example of the Song “Taxi Tango”
Bibliographic record
Abstract
We explored the characteristics of children’s songs appropriate for adaptive curriculum from the perspective of Preschool teachers using the song ‘Taxi Tango’. Semi-structured interviews were conducted with six preschool teachers who used ‘Taxi Tango’ in teaching. These preschool teachers lacked specialized musical backgrounds and exhibited a positive attitude toward aesthetic education. Their teaching encompassed various age groups of students and employed instructional approaches such as thematic teaching, project-based teaching, and learning center-based teaching. A shared enthusiasm for ‘Taxi Tango’ among preschoolers and preschool teachers was found. Preschool teachers highlighted the song’s high repetition in lyrics and melody and the dynamic musical elements for aesthetic education within the curriculum. The distinctive dynamic design of the song contributed to the creation of diverse musical activities and enhanced the quality of teaching and motivation. The song’s unique dynamic design fostered independent learning and exploration among preschoolers and cultivated music exploration and expression. A children’s song appropriate for education must be simple and comprehensible with distinctive musical characteristics for aesthetic education through diverse activities.
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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 | 0.001 |
| 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 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".