Impact of Using CALL on Iranian EFL Learners' Vocabulary Knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Computer Assisted Language Learning (CALL) integration in EFL contexts has intensified noticeably in recent years. This integration might be in different ways and for different purposes such as vocabulary acquisition, grammar learning, phonology, writing skills etc. More explicitly, this study is an attempt to explore the effect of using CALL on vocabulary acquisition of EFL learners. It also discusses CALL applications for vocabulary instruction and searches for efficient methods to integrate CALL in vocabulary acquisition. For the discussion, it reviews the results of previous international and Iranian studies on CALL integration in vocabulary instruction to illustrate the state of research in this field. Based on the literature review, it is proposed that further research is required to find out the influences of CALL on different facets of vocabulary knowledge. At the end, some pedagogical implications have been offered for EFL teachers and learners.</p>
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it