The Utilisation of Duolingo to Enhance the Speaking Proficiency of EFL Secondary School Students in Saudi Arabia
Bibliographic record
Abstract
Even though Saudi EFL students devote multiple years to improving their English-speaking proficiency, they struggle to achieve fluency. The present study, however, focuses on investigating the effectiveness of using Duolingo in EFL classrooms to enhance the participants’ speaking proficiency. The participants who underwent this study were 28 male Saudi students studying in secondary school, namely at Sharia Institute. They were divided into control and experimental groups. Data was collected via post-test to conduct a valid comparison between the two groups. The 14 students in the experimental group had been using Duolingo for a period of four consecutive months while the participants in the control group have never used Duolingo. To make a valid comparison of the mean score between the two groups, an independent samples t-test was used in this experiment. After analysing the results, it has been concluded that the integration of Duolingo in the learning process has a fundamental positive impact on enhancing participants’ speaking proficiency as well as improving their overall language skills. Additionally, the participants’ positive attitude towards Duolingo was an intrinsic factor that helped alleviate their anxiety when speaking.
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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 | 0.001 |
| 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 | 0.000 |
| 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".