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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".