EYE GAZE AND PRODUCTION ACCURACY PREDICT ENGLISH L2 SPEAKERS’ MORPHOSYNTACTIC LEARNING
Bibliographic record
Abstract
This study investigated the relationship between second language (L2) speakers’ success in learning a new morphosyntactic pattern and characteristics of one-on-one learning activities, including opportunities to comprehend and produce the target pattern, receive feedback from an interlocutor, and attend to the meaning of the pattern through self- and interlocutor-initiated eye-gaze behaviors. L2 English students (N =48) were exposed to the transitive construction in Esperanto (e.g.,filino mordas pomon[SVO] orpomon mordas filino[OVS] “girl bites apple”) through comprehension and production activities with an interlocutor, receiving feedback in the form of recasts for their Esperanto errors. The L2 speakers’ interpretation and production of Esperanto transitives were then tested using known and novel lexical items. The results indicated that OVS test performance was predicted by the duration of self-initiated eye gaze to images illustrating the OVS pattern during the comprehension learning activity and by accurate production of OVS sentences during the production learning activity. The findings suggest important roles for eye-gaze behavior and production opportunities in L2 pattern learning.
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.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".