The Impact of Using Cambly on EFL University Students’ Speaking Proficiency
Bibliographic record
Abstract
This study sought to investigate the impact of using Cambly, a computer-mediated communication tool, on the speaking proficiency of English as a foreign language (EFL) learners. Further, it aimed to explore the participants’ perceptions of using Cambly. The study employed an experimental design featuring a mixed-methods approach to data collection that involved pre- and post-testing of the participants’ speaking proficiency as well as semi-structured face-to-face interviews. The study sample consisted of 28 EFL university students who were divided into the control and experimental groups. The participants in the experimental group used Cambly to conduct audio calls with native speakers of English over a period of 4 weeks. The quantitative analysis of the participants’ speaking proficiency tests revealed no significant differences between the experimental and control groups’ post-test scores. Moreover, no significant differences were found between the experimental group’s pre- and post-test scores. The qualitative analysis of the participants’ interviews revealed that the use of Cambly had a positive influence on their speaking proficiency, motivation, anxiety level, speaking opportunities, autonomy, social relationships, and cultural awareness.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".