Reading experience drives L2 reading speed development: a longitudinal study of EAL reading habits
Bibliographic record
Abstract
Introduction The present longitudinal study tested the hypotheses that (i) learners become faster readers after intensive English language instruction, and that (ii) learners who read more English texts tend to make larger gains in reading speed. Methods Study participants were 142 L1 Cantonese or Mandarin English learners enrolled in an eight-month university bridging program. Participants completed a reading habits log each week, reporting information about their reading activity, including the type of texts they read, the amount of time they spent reading each text, and the number of pages they read. Results It was found that English language learners spent less time reading per page of text by program end, as shown by a significant linear weekly increase in reading speed. Critically, there was also a significant effect of reading experience: learners who read more pages of text than their peers during the bridging program tended to make the largest net gains in reading speed. Discussion The results support the idea that reading experience is a factor that contributes to reading speed development in English language learners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".