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Record W2917832233 · doi:10.5539/ies.v12n3p135

The Effectiveness of Using Visual Organizations in Improving Reading and Writing Skills for Students with Learning Disabilities from the Teachers’ Point of View

2019· article· en· W2917832233 on OpenAlexvenueno aff
Mohammad Muflih

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)Point (geometry)Mathematics educationTeaching methodDescriptive statisticsSample (material)

Abstract

fetched live from OpenAlex

The study aims to identify the effectiveness of using visual organizations in improving the reading and writing skills of students with learning disabilities from the point of view of the teachers of learning resource rooms, according to the variables of gender, scientific qualification and educational experience through the use of analytical descriptive method. The study was carried out on a sample of 87 male and female teachers, (38) males and 49 females. A questionnaire of 53 items was used and analyzed statistically, the results showed that the most prominent item in the effectiveness of the use of visual organizations to improve reading and writing for students with learning difficulties is: “able to read sentences enhanced with pictures and without pictures, and distinguish between the image of the character and its writing”, the most prominent obstacle to the effectiveness of using visual organizations is not to use visual organizations that fit with the teaching methods of students with learning disabilities, as for the variables, the study showed the existence of differences attributed to the impact of gender and for the benefit of females, the results showed that there are statistically significant differences due to the effect of scientific qualification, and the absence of statistical differences due to the effect of years of experience.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0000.000

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 head0.021
GPT teacher head0.435
Teacher spread0.414 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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