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Record W4280526864 · doi:10.1177/87551233221095051

“Unlearning and Relearning”: Adolescent Students’ Perspectives on Transitioning to a New Teacher and Environment

2022· article· en· W4280526864 on OpenAlexaff
Vanessa Mio, Brenda Brenner

Bibliographic record

VenueUpdate Applications of Research in Music Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyAnxietyPerceptionAttributionProcess (computing)Transition (genetics)Mathematics educationPedagogySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Throughout the learning process, it is common for students to transition to a new teacher, whether they relocate, decide to change teachers, or study at a summer program. During this transition, students must adapt to a potentially different pedagogical philosophy, language, and performance expectations. Using a multiple case study research design, we explored the perceptions of four adolescent violin students who experienced this transitional process while studying with Mimi Zweig at the four-week Indiana University Summer String Academy. We analyzed the interview data, observations, and external data sources through the lens of attribution theory. Results indicated that the participants not only acquired greater self-efficacy and motivation after studying with a new teacher but also endured performance anxiety. These findings inform studio instrumental instructors of the challenges students experience when studying with a new teacher and the physiological and emotional challenges that often accompany change.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.342
Teacher spread0.272 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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