Self-selection of feedback: Yoked groups and the influence of absolute and relative schedules of feedback
Bibliographic record
Abstract
A traditional control group yoked to a group that self-selects their feedback schedule receives feedback on the same number of trials and on the same trials as the self-selected group. This yoked group demonstrates inferior learning compared to the self-selected group. Although the groups are matched for the amount of feedback, information is provided on trials where the yoked individual might not request feedback and also not receive feedback on trials where it would be a learning experience. Subsequently, this leads to a decrement in retention. This study examined the learning of a 6-component serial response task for a yoked group with the same absolute amount of feedback opportunities, but who self-selected their schedule. The yoked with self-selection group was predicted to have a higher level of retention than the traditional yoked group because of the conceptual link between feedback and learning opportunities. The traditional yoked group had significantly a higher AE than the yoked self-selected group in the first block of acquisition. In retention, the yoked self-selected group committed a significantly lower number of errors than both the traditional yoked and self-selected groups. The current results suggest that a yoked self-selected group can produce efficient learning effects and can be a viable control group for future studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".