Exploring the potential relationship between eye gaze and English L2 speakers’ responses to recasts
Bibliographic record
Abstract
This exploratory study investigated whether joint attention through eye gaze was predictive of second language (L2) speakers’ responses to recasts. L2 English learners ( N = 20) carried out communicative tasks with research assistants who provided feedback in response to non-targetlike (non-TL) forms. Their interaction was audio-recorded and their eye gaze behavior was tracked simultaneously using the faceLAB system. Transcripts were coded for characteristics of the feedback episodes (linguistic target, feedback type, intonation, prosody) and types of response (no opportunity, no reformulation, non-TL response, TL response). Eye gaze length for the researcher (when producing the feedback move) and the L2 speaker (when responding to feedback) were obtained in seconds using Captiv software. Following data pruning to reduce the data set to clausal recasts in response to grammatical errors, a logistic regression model revealed that both L2 speaker and mutual eye gaze were predictive of TL responses. Methodological issues for eye-tracking research during L2 interaction are provided, and suggestions for future research are discussed.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".