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Record W2774901840 · doi:10.5430/wje.v7n6p21

The Effectiveness of Pictured Letters Mnemonics Strategy in Learning Similar English Language Letters among Students with Learning Disabilities

2017· article· en· W2774901840 on OpenAlexvenueno aff
Maysoon A. Dakhiel, Mohammed O. Abu Al Rub

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsMnemonicMathematics educationPsychologySet (abstract data type)Sample (material)Test (biology)Teaching methodTreatment and control groupsControl (management)Repeated measures designLearning disabilityDevelopmental psychologyComputer scienceCognitive psychologyArtificial intelligenceMathematicsStatistics

Abstract

fetched live from OpenAlex

The present study aims to investigate the effectiveness of pictured letters mnemonics strategy in learning similarEnglish language letters among students with learning disabilities in Saudi Arabia according to experimental group(1) and (2), control group, gender, and interaction between them. The study sample comprised (90) students withlearning disabilities who were randomly distributed to three equal groups. While the first experimental group wastaught using the modified letter method, the second experimental group was taught using the method of bothmodified and abstract letters, and the control group was taught in the traditional way. To measure the effectiveness ofthe strategy, the authors applied (pre, post, and follow-up) tests. As a result, two-way ANOVA indicated that therewere significant differences attributed to the teaching method, in favor of the method of both modified and abstractletters, while there were no statistically significant differences due to gender or interaction between the teachingmethod and gender. The same results were obtained from the follow-up test. Accordingly, a set of recommendationswere made.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.317
Teacher spread0.309 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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