Please unmute your microphone: Comparing the effectiveness of remote versus in-person percussion training
Bibliographic record
Abstract
Although remote music training has its limitations, the use of technology can lower barriers to its accessibility. This exploratory study compared the effects of remote and in-person percussion training on motor performance, performance quality, and students' enjoyment. The training involved the motor aspects of playing legato on percussion instruments. Twenty percussionists received the training either remotely from an instructor using videoconferencing technology or in person from the same instructor who was in the training room. Motor behavior, legato expressivity, performance quality, and participants' self-rated enjoyment were compared to determine potential advantages and disadvantages of training in the two formats. Furthermore, participants rated their interest in continuing to receive training in the same way they had experienced it, remote or in person. Regardless of whether the instructor was remote or in person, participants lifted their mallets to a greater height above the drums post-training, perhaps because there was more spatial and velocity variability in the movements of their elbows and wrists. Changes in their patterns of post-training movements were paralleled by higher ratings for expressivity of legato and performance quality. Critically, participants who received training from the remote instructor expressed greater interest in continuing training than those who received training from the instructor who was physically present, in both the short and long term. These findings may suggest that remote and in-person instruction yielded comparable changes on motor behavior, as demonstrated by the altered speed at which movements of the elbow and wrist were executed, which in turn may influence the perception of expressivity in legato playing. The results may support the use of remote training as an adjunct to physical practice to lower some barriers to music education.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".