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Using a picture-embedded method to support acquisition of sight words

2019· article· en· W2975255491 on OpenAlexaboutno aff
Chris Strauber, Piya Sorcar, Claire Howlett, Shelley Goldman

Bibliographic record

VenueLearning and Instruction · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhonicsPhraseSightWord (group theory)PsychologyTest (biology)Primary educationLinguisticsComputer scienceMathematics educationNatural language processing

Abstract

fetched live from OpenAlex

This study investigated whether an intervention using words embedded with pictures can be more effective in sight word instruction than one using words alone. Participants included sixty-nine children in junior kindergarten (ages 4–5) enrolled in school in Ontario, Canada. Children were split randomly into treatment and control groups; the treatment group was taught four words using picture-embedded words, and the control group was taught using text alone. Both groups also received phonics instruction to support sight word acquisition. Children in the picture-embedded word condition performed significantly higher than those in the word-alone condition on an immediate post-training test and later retention tests. This outcome, which contrasts with previous studies using picture-embedded words, may result from this method's use of a relevant linking phrase and action that help build an association between picture and word, as well as its incorporation of phonics instruction, with future work needed to test this hypothesis.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.019
GPT teacher head0.351
Teacher spread0.332 · 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 designBench or experimental
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

Citations11
Published2019
Admission routes1
Has abstractyes

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