Using Measures of Print to Predict Reading Ability and Children At-Risk for Reading Disabilities in Spanish-Speaking Second Language Learners
Bibliographic record
Abstract
Due to the changing nature of Canadian society, there has been a dramatic increase in the amount of literature focusing on second language reading acquisition. In this particular study, Spanish-speaking students learning English as a second language (L2) were compared to students who speak English as a first language (L1) on various measures of reading ability and specifically on measures of print exposure, which assess extracurricular reading. Past literature on print exposure has found that print exposure questionnaires serve as significant predictors of variance in reading comprehension, word reading, among other variables (e.g., Cunningham & Stanovich, 1993). The current study used 50 L2 learners and 31 L1 learners to compare their performance on various measures of reading. The L2 students were recruited in two waves, one in 2005 and the next in 2006, hereafter referred to as cohorts. Differences were found within the L2 group, where the first cohort of L2 students performed significantly lower than the L1 group on many measures such as receptive vocabulary, word and pseudoword reading, however, the second cohort of students showed scores more similar to the L1 group on many measures. In predicting variance in reading comprehension scores, the title recognition and nonverbal reasoning were partialled out. However, in the L1 group the title recognition test did not predict unique variance in reading comprehension. Another model was to run predict word reading, and once again the title recognition test was a unique predcitor only in the L2 group. Further analyses broke up the L2 group into students with average and poor reading comprehension skills in order to examine the profiles of students who may be at-risk for reading disabilities and found that rapid automatized naming was a significant predictor of reading comprehension in those students with poor reading comprehension skills. Implications will be 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".