On-site and distance piano teaching: An analysis of verbal and physical behaviours in a teacher, student and parent
Bibliographic record
Abstract
This study was designed to examine how distance piano teaching might affect the verbal behaviours and physical actions of a teacher, a student and a parent. Weekly 30-minute piano lessons over a year-long period were taught to a 5-and-a-half-year-old on-site student and a 6-year-old distance student. All lessons were delivered by the same teacher who followed the Suzuki programme. All sessions were recorded and then analysed using Simple Computer Recording Interface Behaviour Evaluation (SCRIBE), a video analysis software that provides frequencies and durations of pre-coded events. The observation of recorded lessons showed that distance teaching did not slow down student progress. In addition, behavioural analysis revealed that in most aspects, distance and on-site delivery were remarkably similar. The most striking difference was the interaction between the teacher and the parent. During on-site teaching, most of the teacher’s instructions were directed to the student while the parent was listening and observing attentively; during distance teaching, half of the teacher’s instructions were addressed to the student and the other half to the parent. The distance student also tended to relate more to the parent than to the teacher. In the distance environment, when interacting with a young beginner student, the role of the parent becomes very central to the success of the lessons.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".