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Record W3005687399 · doi:10.1111/jcal.12417

The effects of ABRACADABRA on reading outcomes: An updated meta‐analysis and landscape review of applied field research

2020· article· en· W3005687399 on OpenAlexafffund
Philip C. Abrami, Larysa Lysenko, Eugene Borokhovski

Bibliographic record

VenueJournal of Computer Assisted Learning · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVocabularyReading (process)Context (archaeology)Meta-analysisSuiteMathematics educationComputer sciencePsychologyInstructional designLinguistics

Abstract

fetched live from OpenAlex

Abstract ABRACADABRA (ABRA) is an evidence‐based suite of interactive multimedia that engages learners in the development of core reading skills. This meta‐analysis presents an update of the research evidence about the effectiveness of ABRA for elementary students. It reports 91 effect sizes in six reading‐related outcomes for a sample of 7,388 students. Regardless of context and measurement type, the studies yielded positive effects of ABRA, ranging in magnitude from g+ = 0.080 for Vocabulary Knowledge to g + = 0.378 for Phonemic Awareness and reaching statistical significance in four outcome categories. This meta‐analysis adds to our understanding of the effectiveness of ABRA‐based reading instruction by exploring factors of research design, ABRA design and implementation contexts, and various student characteristics and offers implications for instructional practice.

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)0.026
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.061
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.022
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.056
GPT teacher head0.381
Teacher spread0.325 · 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.

Study designMeta-analysis
DomainEvaluation
GenreReview

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

Citations33
Published2020
Admission routes2
Has abstractyes

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