Effects of self-regulation of practice trial order and proactive and retroactive auditory models in learning sequential-timing patterns
Bibliographic record
Abstract
Two empirical effects, previously shown to be advantageous to learning, were contrasted in this study: a) self-regulation of practice trial order (vs. experimenter-defined), and b) retroactively-presented model information (vs. proactively-presented). The purpose was to assess if self-regulated practice modified the effects of the timing of modeled information for the learning of three sequential-timing patterns. Each of the patterns consisted of unique temporal parameters between the presses (and accompanying auditory tone) of the same sequence of five keys. Auditory information was modeled either before (proactively) or after (retroactively) each trial, and practice order was learner-regulated. Absolute constant error (|CE|) and variable error (VE) were calculated for both the total time (TT) and the sum of the segmental times (SST). Retention tests revealed that the proactive group performed with less |CE| for SST, while the retroactive group performed with less |CE| for TT for at least one pattern; suggesting that self-regulated practice eliminated the clear advantage for providing modeled information retroactively. The retention results also revealed that learners who chose to switch patterns frequently during practice, regardless of when the model was presented, had less |CE| and VE for SST and less VE for TT than learners who self-regulated with infrequent task switches; suggesting that learner-imposed contextual interference enhanced retention.Acknowledgments: This study was supported by NSERC
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".