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Record W3012047107

THE LINGUISTIC AND READING SKILLS OF ENGLISH LANGUAGE LEARNERS AT-RISK FOR POOR READING COMPREHENSION: PROFILES AND PREDICTORS

2017· dissertation· en· W3012047107 on OpenAlexfundno aff
Christie Fraser

Bibliographic record

VenueTSpace · 2017
Typedissertation
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersOffice of International Science and EngineeringSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsReading comprehensionLinguisticsReading (process)English languageComprehensionComputer sciencePsychologyMathematics education
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.340
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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