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Record W7009421252

THE EFFECTS OF EARLY IDENTIFICATION AND INTERVENTION ON READING SCORES AT THE KINDERGARTEN LEVEL

2023· article· en· W7009421252 on OpenAlexaboutno aff

Bibliographic record

VenueMurray State's Digital Commons (Murray State University) · 2023
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Intervention (counseling)Response to interventionLiteracyQuarter (Canadian coin)Identification (biology)Emergent literacyPrimary education
DOInot available

Abstract

fetched live from OpenAlex

Research indicates that reading fluently is the key to success: academically, economically, socially, as well as to a healthier lifestyle (Forrest, 2018; Wanzek et al., 2018). While research has shown that Response to Intervention (RTI) is a positive instructional program that will increase primary students’ academic abilities at grades 1 and 2 (Richards et al., 2007), there is a need for more research regarding RTI with kindergarten students. This quasi-experimental quantitative research study examined if early identification and intensive intervention through the addition of Response to Intervention (RTI) at the kindergarten level will lead to increased reading scores and better grades. Two kindergarten classrooms from a small elementary school in Western Kentucky provided 14 students for the sample. They became the experimental and comparison groups because their September STAR Early Literacy Assessment scores and their first quarter Reading Foundational Skills grades revealed they were struggling to learn how to read. Findings revealed using RTI with both groups of students was statistically significant for their STAR Early Literacy Assessment Scores and Reading Foundational Skills grades. Discussion includes the study’s relation to P-20 goals and suggestions for future research.

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)0.000
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 score0.682
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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