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Analyzing the Applicability of Preschool Curriculum Using Children’s Songs from the Perspective of Preschool Teachers: An example of the Song “Taxi Tango”

2024· article· en· W4401229830 on OpenAlexaff
Mei‐Ying Liao, Martin Bernard Lu

Bibliographic record

VenueInternational Journal of Social Sciences and Artistic Innovations · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCreative Drama in Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsPerspective (graphical)CurriculumPreschool educationPsychologyMathematics educationPedagogyDevelopmental psychologyVisual artsArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.338
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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