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Record W2913419163 · doi:10.1002/pits.22240

Enhancing student access to science curricula through a reading intervention

2019· article· en· W2913419163 on OpenAlexaff
Sonja Saqui, Sterett H. Mercer, Michèle P. Cheng

Bibliographic record

VenuePsychology in the Schools · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluencyReading (process)PsychologyMultiple baseline designCurriculumIntervention (counseling)Psychological interventionCurriculum-based measurementWord recognitionMathematics educationPedagogyCurriculum developmentLinguistics

Abstract

fetched live from OpenAlex

Abstract The current study explored whether a reading intervention combining flexibly applied multisyllabic word‐decoding strategies with evidence‐based fluency strategies was effective in improving the science text reading skills of upper‐elementary struggling readers. Four students, three in fourth and one in fifth grade, participated in the study. A delayed multiple baseline design was utilized, with a staggered 3‐week baseline followed by 8 weeks of reading intervention. Three students demonstrated small to moderate gains in reading fluency on science instructional passages, but no generalized gains in reading fluency on standardized passages. All students demonstrated direct gains in multisyllabic word‐decoding accuracy on science instructional passages, but no generalized gains in decoding accuracy on standardized passages. Participating students rated the intervention favorably and perceived gains in their reading skills. These findings support the use of science curricular passages when implementing reading interventions to enhance students’ ability to access the curriculum.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.037
GPT teacher head0.456
Teacher spread0.418 · 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.

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
Published2019
Admission routes1
Has abstractyes

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