The Effectiveness of Error Correction on the Learning of Morphological and Syntactic Features
Bibliographic record
Abstract
The study investigated the effects of correction of learners’ grammatical errors on acquisition. Specifically, it compared the effects of morphological versus syntactic features. Data for the study were collected from 112 transcriptions of oral interviews with Iranian intermediate level students of English as a Foreign Language. During or following the interview the researcher corrected the participants on their grammatical errors Individualised tests focusing on morphological and syntactic errors that had been corrected were constructed for each participant and administered. Statistical analyses of the learners' scores on their individualised tests were carried out. Results showed that treatment of morphological features was found to be more effective than that of syntactic features. It is argued that morphological features are generally learnt as items whereas syntactic features involve system learning. This finding lends support to suggestions that corrective feedback (like other types of form-focused instruction) needs to take into account learners’ cognitive readiness to acquire features (Pienemann, 1984; Mackey, 1999).
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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 | 0.005 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".