An analysis of student errors as addressed by private piano teachers
Bibliographic record
Abstract
This study investigated the degrees to which private piano teachers addressed student errors, and determined whether increased focus on student errors correlated positively or negatively with measures of teacher effectiveness and student attitude.Twenty private piano teachers were video recorded in two one-on-one piano lessons, two different students each.Systematic observation procedures were used to collect data from the 40 lessons to determine the number of student errors and teacher behaviour related to those errors.Error types analysed were pitch, rhythm, dynamics, articulation, tempo, and pedal.Ten evaluators viewed excerpts of the videos and rated the teachers on six aspects of teacher effectiveness: communication, feedback, interaction, goal accomplishment, personality, and general effectiveness.Additionally, students from the recorded lessons completed an attitude questionnaire.Teachers addressed one-fifth of errors overall.Students committed rhythm errors more than any other error type, and teachers addressed rhythm errors least frequently.Students committed tempo errors with the least frequency, and teachers addressed tempo errors the most often.Teachers whose students made more pitch and rhythm errors than other students were rated as less effective than their peers on five of the six effectiveness measures.As teachers addressed more errors in general, they were rated higher on effectiveness measures.The amount of time devoted to pitch errors was negatively related to these measures.Effectiveness measures were positively correlated with three behaviours related to articulation errors.Teachers not trained in performance at a university level addressed pitch errors more frequently than those who were.They were also rated as being less effective on four measures.iii Student perception of teacher attention to errors did not correlate with lesson and teacher satisfaction, anxiety over making mistakes, or comparison of ability to peers.Student perception of teacher correction to errors correlated positively with satisfaction of lesson and teacher, and self-confidence.Effects were found for gender on mistake anxiety and self-confidence.Taken together, these data seem to suggest that the most effective teachers in this study addressed more errors than less effective teachers.Their vigilance did not appear to adversely affect student attitude. Keywords: attitude,
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".