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Record W2758378755 · doi:10.1177/0255761417731439

An investigation of postsecondary violin instructors’ remedial pedagogy: A case study

2017· article· en· W2758378755 on OpenAlexaff
Vanessa Mio

Bibliographic record

VenueInternational Journal of Music Education · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsRemedial educationViolinPsychologyQualitative researchAttributionResistance (ecology)Grounded theoryPedagogyMusic educationSemi-structured interviewMathematics educationMedical educationSocial psychologySociologyManagementSocial science

Abstract

fetched live from OpenAlex

Applied violin instructors at the postsecondary level often implement remedial pedagogy with incoming first-year students in order to address technical/musical habits or deficiencies. As students strive to alter their technique, resistance to change and low self-efficacy often result. Using a descriptive qualitative multiple case study research design, 10 postsecondary violin instructors from across North America were interviewed to gain insight into their personal perspectives and experiences implementing remedial pedagogy with first-year violin students. The interview data and external data sources were analyzed through the theoretical framework of attribution theory and teacher attribution scaffolding theory. The results indicate that many participants address correction through effective communication, based on the individual physiological/psychological wellbeing of every student, their level of self-efficacy, motivation, resistance to change, and postsecondary expectations. The pedagogical expertise and applied experiences presented in this study should inform current and future violin pedagogues about how to effectively address technical/musical deficiencies so that the wellbeing of students remains a priority throughout the remedial process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.356
Teacher spread0.274 · 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.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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