The learning benefits of self-controlled feedback schedules are modulated by strategy choice: A mixed-methods approach
Bibliographic record
Abstract
Allowing learners to control aspects of their practice environment enhances the learning of motor skills relative to yoked groups. To explore the strategies learners use when provided “self-control” during practice, we employed a mixed-methods experimental design. Participants practiced a linear sliding task with self-controlled knowledge of results (KR) on day one, then completed a retention and transfer test 24-hours later. During the middle and at the end of the acquisition phase, participants completed an open-ended questionnaire that asked why they chose to request feedback on the previous trials. Inductive thematic content analysis was conducted on the questionnaire data resulting in the emergence of five themes (strategies): 1) establish a baseline understanding, 2) evaluate a change in (motor) strategy, 3) confirm a perceived “good” trial (CGT), 4) confirm a perceived “bad” trial, and 5) schedule KR based on trial. Extant literature suggests that learners tend to prefer feedback after relatively good trials. Further, researchers have also shown that feedback provided after relatively good trials, as compared to poor trials, benefits motor learning. Therefore, in the second level of analysis, we investigated the effect of utilizing a CGT strategy on subsequent retention and transfer performance. Participants who reported using the CGT strategy were compared to those who reported using other strategies and to a yoked group. Consistent with previous suggestions, participants who reported using the CGT strategy during the second half of practice performed significantly better at retention than those who did not or who were in a yoked group, F(2,32) = 4.81, p = .016, ηp2 = .25. Interestingly, participants that used strategies other than CGT performed similarly at retention to those in the yoked group, suggesting that the advantage of self-control may in part depend on the learner’s strategy for requesting KR.Acknowledgments: This research was funded by SSHRC and NSERC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".