Examining Reading Comprehension Profiles of Grade 5 Monolinguals and English Language Learners Through the Lexical Quality Hypothesis Lens
Bibliographic record
Abstract
This study set out to compare patterns of relationships among phonological skills, orthographic skills, semantic knowledge, listening comprehension, and reading comprehension in English as a first language (EL1) and English language learners (ELL) students and to test the applicability of the lexical quality hypothesis framework. Participants included 94 EL1 and 178 ELL Grade 5 students from diverse home-language backgrounds. Latent profile analyses conducted separately for ELLs and EL1s provided support for the lexical quality hypothesis in both groups, with the emergence of two profiles: A poor comprehenders profile was associated with poor word-reading-related skills (phonological awareness and orthographic processing) and with poor language-related skills (semantic knowledge and, to a lesser extent, listening comprehension). The good comprehenders profile was associated with average or above-average performance across the component skills, demonstrating that good reading comprehension is the result of strong phonological and orthographic processing skills as well as strong semantic and listening comprehension skills. The good and poor comprehenders profiles were highly similar for ELL and EL1 groups. Conversely, poor comprehenders struggled with these same component skills. Implications for assessment and future research are 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".