THE LINGUISTIC AND READING SKILLS OF ENGLISH LANGUAGE LEARNERS AT-RISK FOR POOR READING COMPREHENSION: PROFILES AND PREDICTORS
Notice bibliographique
Résumé
This dissertation concerns the linguistic and reading profiles and predictors of English language learners (ELLs) classified as typically developing or at-risk for poor reading comprehension. The ELLs in the studies came from Chinese, Portuguese, and Spanish home language backgrounds, but had all begun formal schooling in English in kindergarten. An at-risk classification model based on performance on components of the simple view of reading (Gough Tunmer, 1986), and using cut-off scores at the 30th percentile or below and the 40th percentile or above, was employed for identification of poor and good readers, respectively. ELLs (n = 127) were subtyped in grade 4 as either typically developing or at-risk based on their decoding and language comprehension skills in relation to the ELL sample (and not to monolingual norms). Reader subtypes used in the final analyses were: poor decoders (difficulties with word reading; n = 17), poor language comprehenders (language impaired; n = 15), multi-deficit at-risker (problems in decoding and language comprehension; n = 20), and typical developers (no deficits in decoding or language comprehension; n = 57). Study 1 compared the grade 4 profiles of the ELL reader subtypes on the following skills: word reading, reading fluency at the word- and text-levels, vocabulary, inferencing strategy, and reading comprehension. To validate the at-risk classification model, multivariate analysis of covariance (MANCOVA) results indicated that all three at-risk reader subtypes were experiencing significant problems with their reading comprehension in grade 4 when compared to typically developing ELLs. Different skill profiles were observed across the three at-risk reader groups in grade 4: poor decoders demonstrated difficulties with various aspects of word reading (accuracy and fluency), and inferencing strategy; poor language comprehenders demonstrated difficulties in word reading fluency; and multi-deficit at-riskers demonstrated pervasive difficulties with all the reading and language skills under study, including fluency and inferencing strategy. Study 2 identified longitudinal (from grade 2) linguistic and reading predictors of later at-risk ELL reader subtype in grade 4. Multinomial logistic regression models indicated that there were different predictors of later at-risk status across the reading groups: word reading fluency for poor decoders; receptive vocabulary for poor language comprehenders; and fluency and oral expression for multi-deficit at-riskers. Similar to the findings of previous research with poor reading ELLs (e.g., Geva Herbert, 2012; Geva Massey-Garrison, 2013; Li Kirby, 2014), findings suggest that not all ELL readers with poor reading comprehension are the same; there are different sources of reading comprehension problems which point to different intervention foci. Furthermore, it appears that readers struggling with reading comprehension due to poor language can be successfully identified as early as grade 2, prior to the onset of their later difficulties in reading comprehension. Findings provide support for an enhanced simple view of reading that also includes fluency and inferencing strategy. Directions for future research and implications for practice are presented.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».