The Effect of Asynchronous/Synchronous Approaches on English Vocabulary Achievement: A Study of Iranian EFL Learners
Bibliographic record
Abstract
The contribution of computer-assisted instructional programs to language learning process has been the focus of researchers for about two decades. However, the effect of synchronous and asynchronous computer-assisted approaches of language teaching on improving L2 vocabulary has been scarcely investigated. This study explored whether synchronous, asynchronous, and integrated approaches had any significant impact on vocabulary achievement of Iranian EFL learners in an intensive reading program. The participants were 120 students, majoring in English Teaching and Translation Studies at Islamic Azad University of Abadan. The participants were at intermediate level of language proficiency. They took a vocabulary pretest and posttest before and after the treatment. The results showed that there was a significant difference between the traditional approach and the other three approaches. That is, computer-assisted teaching approaches significantly influenced EFL learners’ vocabulary learning. The findings also manifested that integrated approach exerted significant influence on improving L2 vocabulary achievement of language learners. The findings implied that language learners, who were thought under computer-assisted approaches, had more autonomy and self-efficacy and showed more intrinsic motivation to learn the target language than the learners who were taught under traditional approach. Thus, computer-assisted approaches can help language teachers create more fruitful learning atmosphere to reduce distressful factors and accelerate learning process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".