Corrective Feedback in SLA: Classroom Practice and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the realm of language teaching, error correction has a long and contentious history. Some schools of thought like nativism refute error correction while others firmly adhere to error correction and regard error as a sin that should be avoided. This dilemma bewilders TEFL practitioners and teachers how to treat errors. Due to the controversial nature of this issue, whether and how to correct errors have spawned numerous celebrated publications in this area in the domains of first language acquisition (FLA) and second language acquisition (SLA). In this vein, lots of studies have probed the role of corrective feedbacks in language classrooms. This paper reviews the main surveys on corrective feedback, providing the theoretical rational for and against error correction, shedding light on different types of corrective feedbacks, and encapsulating the theoretical and empirical studies conducted to investigate corrective feedback and its impact on different aspects of language, offering issues for further directions to cast away all the doubts in this domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.032 |
| 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 it