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Record W2170738147 · doi:10.5539/ies.v7n8p35

Pre-Service Music Teachers’ Concerns before a Practicum Stint

2014· article· en· W2170738147 on OpenAlexvenueno aff
Ramesh Rao, Wellington Edna, Lim Zek Chew, Nik Rozalind Nik Hassan

Bibliographic record

VenueInternational Education Studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumPsychologyStudent teacherTeacher educationPedagogyMusic educationMathematics educationThematic analysisReflection (computer programming)Medical educationQualitative researchSociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

A crucial element in any teacher training programme is teaching practice. Terms such as practicum, refers to teaching practice which is embedded in the teacher training programme itself. Such stints become more challenging for a teacher, for their teaching would be observed and assessed by a teacher educator. The purpose of this study was to explore the music pre-service teachers’ concerns before embarking on practicum. A total of 24 pre-service teachers participated in this study. Their concerns were captured through a written reflection. This reflection was written seven days before these pre-service teachers were to begin their practicum stint. The written reflections were analysed using a thematic approach. Among the concerns are issues related to communication skills, lesson planning and meeting the expectations of teacher educators. The recommendations made in enhancing teacher education programme, notably in training music teachers are also made.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.148
GPT teacher head0.366
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2014
Admission routes1
Has abstractyes

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