The Effect of Computer- assisted Language Learning on Improving EFL Learners’ Pronunciation Ability
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
Computer aided language learning having replaced the traditional paradigms has been acknowledged by manyeducators, and also began to become a unique opportunities in an educational context (Arias, Yoma & Vivanco,2010). This paper tries to show the impacts of Computer Assisted Language Learning (CALL) on Iranian femalestudents' pronunciation skills. 60 students randomly selected from NemoonehVakili junior high school were dividedinto control and experimental groups. The administration of a pronunciation test showed that two groups werehomogeneous in terms of their pronunciation skills at the entry level. While both groups had the same instructorduring 8 sessions, only the experimental group received the materials by using computer. The performance of theexperimental group on pronunciation test held at the end of the course showed that the mean score of this group wassignificantly higher than the control group. Hence, the students' learning based on CALL can increase the motivationand interest of learning among the learners and have a profound impact on the students’ achievement ofpronunciation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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) | 0.000 | 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