Video feedback and the self-evaluation of college-level guitarists during individual practice
Bibliographic record
Abstract
Developing musicians typically engage in self-regulated practicing during the time that passes between lessons with their teachers. An important aspect of self-regulated practice is the ability to identify and correct areas of development in performance in the absence of a teacher’s feedback, but the effort required to perform as well as monitor a performance represents a challenge for any learner. Videotaping the performance and watching it afterwards to fully concentrate on each task could constitute a solution to this problem. In our study, we verified how video feedback could affect the self-evaluation of intermediate-advanced musicians while practicing a new piece of music. To attain this objective, we analyzed and coded the self-evaluative comments of 16 classical guitarists while practicing. We then compared the number of coding entries in each category of a group of participants who used video feedback ( n = 8) on four occasions over a period of ten practice sessions with those of a group of musicians who did not use video feedback ( n = 8). Our results indicate that musicians who used video feedback modified the way they formulated their self-evaluative comments while practicing, and that these changes were more marked with higher-performing musicians.
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 | 0.002 | 0.021 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".