Motor Skill Retention Is Modulated by Strategy Choice During Self-Controlled Knowledge of Results Schedules
Bibliographic record
Abstract
Investigations into the strategies that are used by participants when they control their knowledge of results (KR) schedule during practice have predominantly relied on multiple-choice questionnaires. More recently, open-ended questions have been used to allow participants to produce their own descriptions rather than selecting a strategy from a predetermined list. This approach has in fact generated new information about the cognitive strategies used by learners to request KR during practice (e.g., Laughlin et al., 2015). Consequently, we examined strategy use in self-controlled KR learning situations using open-ended questions at two different time points during practice. An inductive thematic content analysis revealed five themes that represented participants’ unique strategies for requesting KR. This analysis identified two dominant KR strategies: “establish a baseline understanding” in the first half of practice and “confirm a perceived good trial” in the second half of practice. Both strategies were associated with superior retention compared with a yoked group, a group that was unable to engage in KR request strategies because KR was imposed rather than chosen. Our results indicate that the learning advantages of self-controlled KR schedules over yoked schedules may not only depend on what strategy is used, but also when it is used.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".