Determinants of Word‐Reading Development in English Learner University Students: A Longitudinal Eye Movement Study
Bibliographic record
Abstract
ABSTRACT We investigated the word‐reading development of adult second‐language learners of English. A sample of 70 (Mandarin or Cantonese) Chinese‐speaking students enrolled in a university‐level English bridging program at a Canadian university silently read passages of text at the beginning and end of the program while their eye movements were recorded. At each timepoint, we also administered a battery of tests that measure key component skills of second‐language reading (phonological processing, vocabulary knowledge, and listening comprehension). We found longitudinal changes in lexical processing for long words in early (refixation probability and gaze duration) and late (go‐past time and total reading time) eye movement measures, indicating a shift from a sublexical to a holistic word‐processing strategy. We found the largest gains in sublexical processing among students with stronger phonological awareness upon entry to the program and students who acquired more vocabulary than their peers during the program. We interpret the results of this study as evidence of a transition from a lexical processing strategy that is heavily reliant on phonological decoding to word‐reading behavior that is more actively engaged in higher order cognitive processes, such as meaning integration. This research offers novel insights into predictors of reading skill in postsecondary English‐language bridging programs.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".