The Recommendation for Learners to Be Provided with Control Over Their Feedback Schedule Is Questioned In a Self-Controlled Learning Paradigm
Bibliographic record
Abstract
Researchers have shown that learners who self-control (SC) their knowledge of results (KR) schedule learn the task more effectively than yoked learners. A common recommendation from these results is that learners should be provided choice over their KR schedule, rather than at a coaches' discretion (Wulf & Lewthwaite, 2016). No research to date has compared SC learners to a group that more closely mimics receiving KR from a coach, thus challenging whether such a recommendation can be made. To this end, three groups learned a golf putting task; an SC group, a traditional yoked group (TY), and a group who were led to believe that their KR schedule was being controlled by a golf coach (perceived coach-controlled yoked group; PCC). Participants (N = 60) completed three phases; pre-test, acquisition, and two 24-hr delayed post-tests (retention/transfer). All groups lowered their mean radial error (MRE) and bivariate variable error (BVE) throughout acquisition. As hypothesized, the SC group (M = 40.10) had lower adjusted MRE compared to the TY group (M = 43.12) during the post-tests, yet, the PCC group had the lowest adjusted MRE (M = 36.61). These differences, however, were not statistically significant, F(2, 54) = 2.81, p = .069. BVE did not display the same pattern as MRE during the post-test as group means were clustered together, F(2, 57) = 0.38, p = .963. Results from a questionnaire indicated that both yoked groups showed moderate ratings for receiving KR on a desired schedule, as well as preferring KR on good trials, or good and bad trials equally. Taken together, these results call into question the recommendation for practitioners to give choice to a learner over KR scheduling.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".