Incongruence Between Learning Style and Written Corrective Feedback Type: Mediating Effect of Implicit Theory of Learning Style
Bibliographic record
Abstract
Implicit theory (Dweck, 2000) suggests that learners’ theories about the malleability of their individual traits (learning style, here) determine the extent to which they can stretch their learning style (Gregersen & MacIntyre, 2014; Young, 2010) and benefit from the instruction that mismatches their preferred styles. The present study aimed at investigating the extent to which Iranian EFL learners with inductive vs. deductive learning styles would benefit from the written corrective feedback (WCF) that does not match their learning styles (i.e., implicit vs. explicit WCF). The study also examined if their success (or lack of) in style stretching and improving their written accuracy is due to the implicit theory (entity vs. incremental) they hold about their learning style. The result showed that students with an incremental theory significantly improved their written accuracy more than those with an entity theory. Also, the findings revealed that inductive learners were more successful in adapting to the mismatched WCF (explicit) and made greater improvement in their written accuracy than deductive students who received implicit WCF.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".