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Record W4389201613 · doi:10.1097/iyc.0000000000000255

Preparing Preschool Educators to Monitor Child Progress

2023· article· en· W4389201613 on OpenAlexaff
Collin Shepley, Devin Graley, Justin D. Lane

Bibliographic record

VenueInfants & Young Children · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsProfessional developmentSpecial educationMedical educationPsychologyEarly childhood educationWork (physics)Professional associationEarly childhoodPedagogyMedicinePolitical sciencePublic relationsDevelopmental psychology

Abstract

fetched live from OpenAlex

The provision of progress monitoring is mandated under federal law for children receiving special education services. In recent years, professional organizations have recommended that this provision be extended to include children receiving services within multitiered systems of support. Organizations representing the field of early childhood education have embraced this focus on progress monitoring to include all children, regardless of ability or disability. To understand how to effectively prepare preschool educators to engage in progress monitoring practices, we conducted a systematic review of the literature. Results of repeated and extended search methods identified only four studies. We present our findings and stress the need for researchers and funding agencies to work toward establishing a rigorous body of literature devoted to professional development and teacher training surrounding progress monitoring.

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)0.070
metaresearch head score (Gemma)0.192
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.192
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.005
Science and technology studies0.0020.001
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.358
Teacher spread0.342 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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