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Record W2062650362 · doi:10.3389/fpsyg.2014.01325

Self-controlled feedback is effective if it is based on the learner’s performance: a replication and extension of Chiviacowsky and Wulf (2005)

2014· article· en· W2062650362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsPsychologyReplication (statistics)Task (project management)Perspective (graphical)Social psychologyCognitive psychologyDevelopmental psychologyStatisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The learning advantages of self-controlled feedback schedules compared to yoked schedules have been attributed to motivational influences and/or information processing activities with many researchers adopting the motivational perspective in recent years. Chiviacowsky and Wulf (2005) found that feedback decisions made before (Self-Before) or after a trial (Self-After) resulted in similar retention performance, but superior transfer performance resulted when the decision to receive feedback occurred after a trial. They suggested that the superior skill transfer of the Self-After group likely emerged from information processing activities such as error estimation. However, the lack of yoked groups and a measure of error estimation in their experimental design prevents conclusions being made regarding the underlying mechanisms of why self-controlled feedback schedules optimize learning. Here, we revisited Chiviacowsky and Wulf's (2005) design to investigate the learning benefits of self-controlled feedback schedules. We replicated their Self-Before and Self-After groups, but added a Self-Both group that was able to request feedback before a trial, but could then change or stay with their original choice after the trial. Importantly, yoked groups were included for the three self-controlled groups to address the previously stated methodological limitation and error estimations were included to examine whether self-controlling feedback facilitates a more accurate error detection and correction mechanism. The Self-After and Self-Before groups demonstrated similar accuracy in physical performance and error estimation scores in retention and transfer, and both groups were significantly more accurate than the Self-Before group and their respective Yoked groups (p's < 0.05). Further, the Self-Before group was not significantly different from their yoked counterparts (p's > 0.05). We suggest these findings further indicate that informational factors associated with the processing of feedback for the development of one's error detection and correction mechanism, rather than motivational processes are more critical for why self-controlled feedback schedules optimize motor learning.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.333
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it