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Record W3173887044 · doi:10.5539/ies.v14n7p1

The Use of Audio-Visual Materials in the Education of Students with Hearing Loss

2021· article· en· W3173887044 on OpenAlexvenueno aff
Elif Akay

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComprehensionVocabularyVocabulary developmentAdaptation (eye)Visual learningHearing lossRealiaTeaching methodAudio visualMathematics educationPedagogyMultimediaComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Learning is the process of restructuring mental schemas through adaptation to new experiences. Instructional materials help form a suitable and individualized educational environment for students with hearing loss while facilitating lexical development and comprehension of academic content. This case study aims to demonstrate the contribution of visual and audiovisual materials to the efficiency of the Social Studies Course for 4th-grade students with hearing loss. Findings of the study have been collected through video recordings of the lessons, validity meetings, the research log, realia, photographs/images, graphic organizers, and educational videos/documentaries. Data analysis demonstrates that the use of visual and audiovisual materials contributes substantially to students’ (a) comprehension of the questions and explanations, (b) participation in and drawing conclusions from classroom discussions, and (c) understanding of new vocabulary and concepts. Based on the results, it can be argued that the use of visual and auditory materials presented so as to cater to the individual needs of the students, together with various educational strategies, provides significant advantages in the acquisition of academic knowledge by students with hearing loss.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.090
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.177
GPT teacher head0.505
Teacher spread0.328 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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