The Effect of WhatsApp Chat Group in Enhancing EFL Learners’ Verbal Interaction outside Classroom Contexts
Bibliographic record
Abstract
This study was mainly conducted to examine the possibility of utilizing ‘WhatsApp Group’ in enhancing EFL learners’ verbal interaction. To do this experimental and descriptive methods were used to achieve the objective of this study. A questionnaire and pre- and post- test were adopted as tools for data collection. Samples of two groups (experimental & control) were randomly selected. The results were analyzed with SPSS. Both groups were taught the same content using the traditional way integrated with WhatsApp Chat groups via text message as communicative platforms for practicing outside classroom contexts for what have been taught in the traditional class. However, the participants of experimental group were restrictively interacted via voice messages while the participants of control group were only interacted via text messages. The analysis of the data revealed that the participants who underwent the voice messages on WhattsApp treatment significantly outperformed those who underwent in text messages on WhatsApp. Hence, utilizing voice messages on WhatsApp chat group can be recommended as an efficient technique in enhancing EFL learners’ verbal interactions outside classroom contexts. Because EFL traditional classroom is no longer more appropriate in offering sufficient opportunities for EFL learners’ verbal interaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".