Corrective feedback, individual differences in working memory, and L2 development
Bibliographic record
Abstract
The present study investigated the differential effects of explicit corrections, meta-linguistic corrective feedback (CF), and analogy-based CF on L2 learners' acquisition of English third-person singular form -s and whether and how individual differences in working memory (WM) mediate such effects. One hundred secondary school English-as-a-foreign-language (EFL) learners at a junior middle school in inland China were randomly assigned to the explicit correction group (EG), the meta-linguistic CF group (MG), the analogy-based CF group (AG), and the control group (CG). Learners performed both an information-gap activity and a picture-description activity where their errors on target structure were treated according to their group assignment. The Untimed Grammatical Judgement Test (UGJT) and the Elicited Oral Production Test (EOPT) were used to measure learners' resulting performance. Learners' WM was measured with operation span test. Results revealed that (1) compared to the control group, all the CF groups significantly improved their performance of English third-person singular form -s over time; (2) explicit corrections and meta-linguistic CF displayed superior advantages over analogy-based CF on the immediate posttest. However, the three CF groups demonstrated no significant difference in their performance of English third-person singular form -s on the delayed posttest; (3) WM was only able to predict the effects of analogy-based CF but not explicit corrections and meta-linguistic CF; and (4) analogy-based CF was more favorable to learners with higher WM who can regulate their limited attentional resources more efficiently, whereas explicit corrections and meta-linguistic CF equalize learning opportunities for all learners with different levels of WM. The findings of this study suggest optimal, profile-matched pedagogical options for L2 learning through identifying CF conditions that cater to the needs of young learners with different levels of WM.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".