Expecting to teach a novel golf putting task did not enhance retention performance: A replication attempt of Daou and colleagues (2016;2016)
Bibliographic record
Abstract
While research has identified several practice variables that purportedly enhance motor learning, recent replication failures highlight the importance of conducting high-powered, pre-registered replications. The “expecting to teach” phenomenon was first reported in the motor learning literature by Daou and colleagues and suggested learners benefit from practicing with the understanding they will later need to teach the skill. The extant data have been mixed but generally positive. While expecting to teach has been shown to enhance motor learning of a golf putt, the mechanisms linked with this benefit are yet to be determined. As such, this study sought to replicate the expecting to teach effect and to extend those findings by exploring participants’ thought processes. Participants (N = 76) were randomly assigned to one of two groups in which they were told that they were learning a golf putt in order to 1) be tested on the skill or 2) to teach the skill to another individual. On Day 1, participants completed pre-test putts, a pre-acquisition intrinsic motivation inventory (IMI), a 2-minute study of an instructional booklet, 50 practice putts and a post-acquisition IMI. During practice, participants were also afforded opportunities to continue studying the booklet and to complete additional putts. Participants returned 24-hours later to complete a retention, a transfer (50 cm longer golf-putt), and a free recall test, as well as a post-study survey to reveal thoughts they engaged in after practice but before (or during) the retention test. Similar to Daou et al., no significant differences were found with study time, number of acquisition putts, or motivation. However, golf-putting performance during retention resulted in no differences for radial error, g = −.13 (95%CI [−.55, .29]), between the two groups and no differences were shown for the recall test. The present study fails to replicate the benefits reported in the original experiments.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".