Assessing the effectiveness of interactional feedback for L2 acquisition: Issues and challenges
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
Abstract How to correct learner errors has long been of interest to both language teachers and second language acquisition (SLA) researchers. One way of doing so is through interactional feedback, which refers to feedback provided on learners' erroneous utterances during conversational interaction. Various theoretical claims have been made regarding the beneficial effects of interactional feedback, and over the years a considerable body of research has examined its effectiveness. In this context, a central and challenging question has always been how to determine whether such feedback is effective for language learning. Studies investigating the role of feedback have used various measures to assess its usefulness. In this paper, I will begin with a brief overview of the recent studies examining interactional feedback, with a focus on how its effectiveness has been assessed. I will then examine the various measures used in both descriptive and experimental research and discuss the issues associated with such measures. I will conclude with what continues to pose us a challenge in assessing the role of feedback and offer some recommendations to inform future research in this area.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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