Exploring the effect of eye gaze cues on novel L2 morphosyntactic pattern learning
Bibliographic record
Abstract
Recent research that explored how input exposure and learner characteristics influence novel L2 morphosyntactic pattern learning has exposed participants to either text or static images rather than dynamic visual events. Furthermore, it is not known whether incorporating eye gaze cues into dynamic visual events enhances dual pattern learning. Therefore, this exploratory eye-tracking study examined whether eye gaze cues during dynamic visual events facilitate novel L2 pattern learning. University students ( n = 72) were exposed to 36 training videos with two dual novel morphosyntactic patterns in pseudo-Georgian: completed events ( bich-ma kocn-ul gogoit, ‘boy kissed girl’) and ongoing actions ( bich-su kocn-ar gogoit, ‘boy is kissing girl’). They then carried out an immediate test with 24 items using the same vocabulary words, followed by a generalization test with 24 items created from new vocabulary words. Results indicated that learners who received the eye gaze cues scored significantly higher on the immediate test and relied on the verb cues more than on the noun cues. A post-hoc analysis of eye-movement data indicated that the gaze cues elicited longer looks to the correct images. Findings are discussed in relation to visual cues and novel morphosyntactic pattern learning.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".