Feedback Valence Agency Moderates the Effect of Pre-service Teachers’ Growth Mindset on the Relation Between Revising and Performance
Bibliographic record
Abstract
It is often assumed that having a choice in the learning process impacts performance and learning. Concomitantly, it is believed that learning choices (e.g., seeking critical or confirmatory feedback) are due to mindset. However, the relation between choice and mindset is still a matter of debate: it is not known whether mindset interferes with the decision to seek critical feedback, the response to critical feedback, or both. This experiment investigates for the first time whether feedback valence agency moderates the effect of mindset on the relation between learning behaviors and learning outcomes. Participants were n = 120 pre-service teachers who were randomly assigned to one of two conditions, Choose (n = 68) and Assign (n = 52), and designed three posters in Posterlet, a game that assessed their learning behaviors (critical feedback and revising) and poster performance. Then, they completed a post-test that also included a mindset survey. Results reveal similar non-significant correlation patterns of mindset with learning behaviors and learning outcomes in both conditions. Feedback valence agency (i.e., condition) moderates the effect of growth mindset on the relation between revision and performance: students who choose to revise their posters more often (i.e., at least twice) perform significantly better when they endorse high rather than low levels of growth mindset but only when feedback valence is chosen rather than assigned. Theoretical implications indicate that feedback valence agency moderates the effect of growth mindset in driving how students respond to their own learning choices to improve their performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".