A Study on Understanding the Effectiveness of Audiovisual Aids in Improving English Vocabulary in ESL Classrooms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".