The Effect of Indirect Error Correction Strategies on Complex Target Forms in Young Chilean L2 Learners
Bibliographic record
Abstract
ABSTRACT Despite the demonstrated effectiveness of written corrective feedback in addressing linguistic errors in L2 writing, numerous moderating factors, such as the nature of the target form, feedback explicitness, and learner proficiency, remain underexplored. Research predominantly focuses on adults with pre‐intermediate or higher linguistic proficiency, leaving a gap regarding young learners with basic language skills. This quasi‐experimental study examines the impact of two WCF strategies with differing levels of explicitness: indirect corrective feedback with localization and indirect corrective feedback plus metalinguistic explanation (ME) on prepositions of time, often considered a complex and an untreatable structure. Conducted with young Chilean learners, the study shows that (1) indirect feedback with localization improved the accurate use of the targeted forms, but its effectiveness increased when combined with ME, (2) complex forms such as prepositions of time respond effectively to correction despite being categorized as non‐treatable, and (3) young learners with low proficiency benefit from corrective treatments, especially when highly explicit. These findings contribute valuable insights into how various factors interact to influence WCF effectiveness.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".