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Record W2169883385 · doi:10.3102/0034654309359353

The Effects of Morphological Instruction on Literacy Skills

2010· article· en· W2169883385 on OpenAlexaff
Peter N. Bowers, John R. Kirby, S. Hélène Deacon

Bibliographic record

VenueReview of Educational Research · 2010
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie UniversityQueen's University
Fundersnot available
KeywordsSpellingPsychologyVocabularyLiteracyPhonological awarenessReading (process)Psychological interventionMathematics educationDevelopmental psychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

The authors reviewed all peer-reviewed studies with participants from preschool to Grade 8 for this meta-analysis of morphological interventions. They identified 22 applicable studies. Instructional effects (Cohen’s d) were averaged by linguistic outcome categories (morphological sublexical, nonmorphological sublexical, lexical, and supralexical) and comparison group (experimental group vs. control or experimental group vs. alternative training). The authors investigated the effects of morphological instruction (a) on reading, spelling, vocabulary, and morphological skills, (b) for less able readers versus undifferentiated samples, (c) for younger versus older students, and (d) in combination with instruction of other literacy skills or in isolation. Results indicate that (a) morphological instruction benefits learners, (b) it brings particular benefits for less able readers, (c) it is no less effective for younger students, and (d) it is more effective when combined with other aspects of literacy instruction. Implications of these findings are discussed in light of current educational practice and theory.

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)0.018
metaresearch head score (Gemma)0.057
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 score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.019
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.478
Teacher spread0.441 · 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

Citations553
Published2010
Admission routes1
Has abstractyes

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