Communicating musical knowledge through gesture: Piano teachers’ gestural behaviours across different levels of student proficiency
Bibliographic record
Abstract
Teachers’ communication of musical knowledge through physical gesture represents a valuable pedagogical field in need of investigation. This exploratory case study compares the gestural behaviour of three piano teachers while giving individual lessons to students who differed according to piano proficiency levels. The data was collected by video recordings of one-to-one piano lessons and gestures were categorized using two gesture classifications: the spontaneous co-verbal gesture classification (McNeill, 1992; 2005) and spontaneous co-musical gesture classification (Simones, Schroeder & Rodger, 2013). Poisson regression analysis and qualitative observation suggest a relationship between teachers’ didactic intentions and the types of gesture they produced while teaching, as shown by differences in gestural category frequency between teaching students of higher and lower levels of proficiency. Such reported agreement between teachers’ gestural approach in relation to student proficiency levels indicates a teachers’ gestural scaffolding approach whereby teachers adapted gestural communicative channels to suit students’ specific conceptual skill levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".