Effects of Using the WhatsApp Application on Iranian Intermediate EFL Learners’ Vocabulary Learning and Autonomy
Bibliographic record
Abstract
The current study was planned to find out if the use of blended learning as a combination of face-to-face instruction and mobile-assisted language learning using WhatsApp contributed to the vocabulary learning and autonomy of Iranian EFL learners compared to the traditional method. To assess their English skills, PET was given to 80 homogenous intermediate learners at the beginning of the study. The study's intended participants were fifty EFL learners with scores that were within the intermediate competency level. Then, the participants were divided randomly to experimental and control groups to see how successful blended learning vs traditional education is at improving learners' vocabulary knowledge. One-way between-groups analysis of covariance was run. On post-test scores, the findings indicated a statistically significant difference between the experimental and control groups. Another one-way between-groups analysis of covariance was performed to assess the impact of two distinct blended learning vs traditional teaching treatments on EFL learners' autonomy. In post-test results, a significant difference between the control and experimental groups' performance was observed. This study provided insights into how technology may be applied to teach language components and skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".