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Record W4387376358 · doi:10.5430/wjel.v13n8p446

A Study on Understanding the Effectiveness of Audiovisual Aids in Improving English Vocabulary in ESL Classrooms

2023· article· en· W4387376358 on OpenAlexvenueno aff
Dajer-Torres Regina, W. Christopher Rajasekaran

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsTamilVocabularyCronbach's alphaComputer scienceMathematics educationEnglish vocabularyComprehensionPsychologyMultimediaLinguistics

Abstract

fetched live from OpenAlex

Vocabulary is essential for comprehension and linguistic development for English language learners. This study used a survey method to identify the effectiveness of vocabulary learning, proper retention, cognitive skills, and technology-based learning in the English language classroom. Thus, the research aims to explore the effectiveness of visual and audio materials in developing vocabulary. The study employed a quantitative approach, and statistical tools were employed for quantitative analysis. The study examines the advantages of audio-visual resources for enhancing vocabulary retention. The participants are tertiary-level students from a reputed university in Vellore district of Tamil Nadu. Around 120 students participated in the survey. A self-designed questionnaire with 10 items was circulated, and data was collected in the Google Form. The results of the data prove the need for audio-visuals in Indian English classrooms, and the students find the audio-visuals interesting and attentive to learning the vocabulary. The Cronbach alpha value of the questionnaire is 0.845; hence, it is concluded that the data is reliable. The survey results show that using multimedia materials in language classrooms will effectively develop English vocabulary among students in Vellore district. Students find it interesting and have an enjoyable learning environment.

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)0.002
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 score0.032
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.037
GPT teacher head0.282
Teacher spread0.245 · 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
Published2023
Admission routes1
Has abstractyes

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