Video feedback and the choice of strategies of college-level guitarists during individual practice
Bibliographic record
Abstract
Developing musicians are expected to accumulate many hours of self-regulated practice to attain expertise on a musical instrument. The ability to choose appropriate strategies based on the internal and external feedback obtained while performing in the absence of the teacher’s support constitutes an important aspect of self-regulated practice. Nevertheless, performing and simultaneously monitoring the performance for feedback represents a challenge for any learner, therefore possibly affecting the resulting choice of strategies. A possible solution to this problem might be to videotape the performance and watching it afterwards to fully concentrate on each task. Studies that have used video feedback in the domain of sports suggest that there may be many similar potential benefits of self-recording for musicians’ self-regulation practices. In our study, we examined how video feedback might affect the choice of strategies of intermediate–advanced musicians ( n = 16) while practising a new piece of music. To attain this objective, we compared the number of qualitative text entries coded against an observation framework derived from observations of a group of musicians who used video feedback four times over a period of ten practice sessions with the number of coding entries of a group of musicians who did not use video feedback. Our results indicated that musicians who used video feedback practised at a slow tempo more often and that they played longer segments of the piece earlier in the learning process than the musicians who did not use video feedback.
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 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 | 0.020 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".