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Record W4415271636 · doi:10.3390/bs15101408

Connecting the Dots: From Teachers’ Perceived Ability to Teach Reading and Their Knowledge of Language and Literacy Concepts to Students’ Reading Growth

2025· article· en· W4415271636 on OpenAlexaff
Pamela Guilbault, George K. Georgiou, Jeremy Huynh, Tomohiro Inoue

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of AlbertaInterior Health
Fundersnot available
KeywordsReading (process)LiteracyTest (biology)PhonicsPhonological awarenessMultilevel modelReading comprehension

Abstract

fetched live from OpenAlex

The purpose of this study was two-fold: (a) to examine the joint contribution of teachers' knowledge of foundational language and literacy concepts and their perceived ability to teach reading to their students' reading growth, and (b) to examine whether the effects of these factors were mediated by teachers' perceived ability to differentiate instruction. A total of 32 language arts teachers and their 582 Grade 3 to 9 students (48% female) participated in the study. Teachers completed a survey on their knowledge of phonological awareness, phonics and morphology, and also rated their ability to teach different reading skills and to differentiate reading instruction. Children were assessed at the beginning and end of the school year on the Test of Word Reading Efficiency-2 and on the Test of Silent Reading Efficiency and Comprehension. Results of multilevel modeling indicated that teachers' knowledge had a direct effect on students' performance at the end of the school year, even after controlling for students' earlier reading ability. Teachers' perceived ability did not predict students' reading growth either directly or indirectly. Taken together, these findings suggest that we need to invest in increasing teachers' knowledge around foundational literacy skills.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.418
Teacher spread0.383 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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