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Record W2907792937 · doi:10.5206/eei.v28i2.7763

Positives, Potential, and Preparation: Pre-service Special Educators’ Knowledge About Teaching Reading to Children with Down Syndrome

2018· article· en· W2907792937 on OpenAlexvenueno aff
Leila Ansari Ricci, Anna V. Osipova

Bibliographic record

VenueExceptionality Education International · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsCredentialReading (process)PsychologyLiteracyMathematics educationSpecial educationPedagogyMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

In this era of reading as a priority, research has indicated that children with Down syndrome (DS) can indeed learn to read, attaining functional levels of literacy and beyond. Families of children with DS are also increasingly advocating for reading instruction for their children. However, few studies have examined what beginning educators know about reading and DS. This study explored the knowledge and perceptions of pre-service special educators about the reading needs and abilities of children with DS. Participants were 225 university students, enrolled in special education teaching credential programs in two southern California universities, who completed a survey designed to assess their knowledge of teaching reading to children with DS, as well as to describe their approach to reading instruction with these students. Results showed promising knowledge on the part of these future teachers, but also highlight the importance of adequate teacher preparation in teaching reading to children with DS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.349
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2018
Admission routes1
Has abstractyes

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