Assessing the effectiveness of interactional feedback for L2 acquisition: Issues and challenges
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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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 | 0.078 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".