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Record W4412913270 · doi:10.3389/feduc.2025.1572807

Exploring the concurrent validity of a linguistically responsive pre-reading assessment in bilingual children: the Urdu Phonological Tele-Assessment (U-PASS) tool

2025· article· en· W4412913270 on OpenAlexafffundabout
Insiya Bhalloo, Monika Molnar

Bibliographic record

VenueFrontiers in Education · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsUrduReading (process)Concurrent validityComputer scienceNatural language processingTest validityLinguisticsPsychologyArtificial intelligencePsychometricsDevelopmental psychology

Abstract

fetched live from OpenAlex

Introduction Childhood language and reading contribute to academic and socio-economic success. It is therefore important to identify school-aged children’s language and reading abilities as early as possible to provide additional support. Oral-language assessment tools are used by speech-language pathologists (SLPs) and educators for early identification of children at risk for reading difficulties. Particularly, phonological processing is an oral-language and pre-reading skill commonly used to examine reading skills at the early grade levels. However, there is a clear English-language assessment bias when it comes to these pre-reading assessment tools, which impacts clinical and research practices. Bilingual children are commonly misidentified with reading disorders–partially due to the lack of appropriate assessment tools. To address this bias, we developed and evaluated an age- and linguistically-appropriate Urdu Phonological Tele-Assessment (U-PASS) tool across three phases. The U-PASS consists of 10 phonological awareness, phonological memory, and rapid automatized naming subtests. Methods We tested 115 typically-developing Urdu-English simultaneous bilinguals in Grades 1–2 across Canada and Pakistan on the U-PASS, vocabulary and reading accuracy measures, and background questionnaires. Results Item-level analysis, involving point-biserial correlations, accuracy rates, and Cronbach’s alpha, and linear regression, involving subtest- and composite-level analysis, were used to collect evidence for U-PASS’s internal reliability and criterion-based concurrent validity. Discussion Our linear regression analyses indicated significant phonological processing-reading associations after accounting for background variables, thereby demonstrating U-PASS’s concurrent validity for assessing Urdu word/non-word reading. The open-access tool enables SLPs and educators to provide early pre-reading assessment and support for the successful development of oral-language and reading skills in bilingual children speaking Urdu—a common, yet under-investigated, language globally.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.377
Teacher spread0.338 · 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 designObservational
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

Citations1
Published2025
Admission routes3
Has abstractyes

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