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Record W4410907548 · doi:10.5430/wjel.v15n6p199

Investigation the Role of Feedback in Enhancing the Effectiveness of Multisensory Instruction for Dyslexic English Language Learners

2025· article· en· W4410907548 on OpenAlexvenueno aff
Wafa’ A. Hazaymeh, Turky Alshaikhi, Mohammad Osman Abdul Wahab, Mohamad Ahmad Saleem Khasawneh

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceNatural language processingMathematics educationLinguisticsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

This study aimed to find out the effect of feedback to the effectiveness of multisensory instruction for dyslexic English language learners (ELLs) in the Asir region, Kingdom of Saudi Arabia. The objective of the study involved: to estimate which kind of feedback (immediate, delayed, corrective, explanatory, motivational) in terms of timing is the most influential in fluency, reading comprehension, and phonemic awareness achievement. With the implementation of a carefully planned experiment 100 dyslexic ELLs aged 10-15 participating in it, data was gathered by the end of an academic year using structured reading tests and questionnaires. The research demonstrated that the reading skills of all the students had significantly increased, and the feedback of immediate and explanatory showed the most significant effect. A series of one-way ANOVA, multiple regression analyses, and Pearson correlation analyses all point to the positive influence of tailored feedback applications when used in multisensory teaching environment. The research outcomes may imply, therefore, that the proper feedback provided when necessary is a crucial factor of the multisensory learning process and it can be especially yielding for young dyslexics.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
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 science0.0000.000
Research integrity0.0000.000
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 head0.286
Teacher spread0.278 · 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 designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

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