Optimizing challenge through performance-contingent practice in dart-throwing
Bibliographic record
Abstract
Based on the challenge point framework, learning is most effective when challenge is appropriate to the skills of the learner, is dynamic, and changes as practice proceeds (Guadagnoli & Lee, 2004). One way that challenge can be manipulated is through task difficulty progressions related to target distance. Practice can get progressively easier or harder (errorful or errorless practice respectively) or be scaled to match the performance of the learner (maintaining moderate error/challenge). Novices in dart throwing (n = 20) were assigned to either a performance-contingent group (progressed to different distances from the dartboard based on performance) or a yoked group (practiced the same distances as pre-test matched partner). They practiced throwing for 210 trials in blocks of three (staying or moving nearer or further from the target depending on success). Both groups improved in accuracy, but they did not differ on measures of performance outcome. However, there were positive correlations between average practice distance, challenge ratings, and accuracy. Participants who were more accurate in retention rated their practice as more challenging and practiced at further distances from the target. Although we did not show benefits from such a challenge-based practice schedule compared to a yoked schedule, both groups improved and as evident from the correlations, the benefits from the yoked group were probably due to an appropriate scaling of challenge. We intend to compare these data to schedules based on error minimization (near to far distance progressions), as well as schedules based on low (near) or high (far) challenge.Acknowledgments: Discovery grant awarded to Hodges from the Natural Sciences and Engineering Research Council of Canada
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| 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".