Utilizing a bandwidth protocol to determine the opportunities for self-controlled knowledge of results during motor skill learning
Bibliographic record
Abstract
Bandwidth protocols reduce KR opportunities and have been proven beneficial for skill acquisition. However, traditional bandwidth procedures do not provide learners with the ability to self-control their KR frequency. When learners are able to request KR in a self-controlled manner, learning benefits are apparent. The purpose of this experiment was to determine the impact of providing learners with the ability to self-control their KR schedule when their performance fell within a pre-determined performance criterion. Due to the fact that learners prefer to request KR after good trials, we predicted that learners who could only request KR after good performances (Bandwidth Inside) would outperform learners who could only request KR after poor performances (Bandwidth Outside). Participants were asked to complete a key-pressing series (3-1-2-4-3-1) in a goal of 2500 ms. Learners (48) completed the task under Bandwidth Inside (12), Bandwidth Outside (12), Yoked with Self-Control Inside (12), or Yoked with Self-Control Outside (12) conditions. Acquisition was 80 trials and retention, time transfer, and pattern transfer tests were conducted 24hrs later. Contrary to our predictions, the bandwidth outside group outperformed the bandwidth inside group in acquisition, F(1,22) = 5.34, p< 0.05, and outperformed both the bandwidth inside group, F (1,22) = 6.26, p< 0.05, and their self-controlled counterparts in retention, F(1,22) = 5.61, p< 0.05. Thus, the results of this study suggest that providing learners with the opportunity to self-control KR when performance falls outside of an instructor-determined range is superior in facilitating skill acquisition. In other words, good retention should result for novice learners when the instructor asks the performer if they would like feedback when the performance was perceived to be poor by the instructor. Overall, these results are commensurate with early theoretical beliefs pertaining to the informational role of KR in correcting movement errors, rather than reinforcing ‘correct’ trials.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".