Use of Translated Saudi Folk Narratives to Improve Reading and Speaking Skills of Saudi EFL Learners
Bibliographic record
Abstract
The present study aims to explore how the use of translated Saudi folk narratives could help improve Saudi EFL learners' reading and speaking skills. (41) EFL elementary-level students from Prince Sattam bin Abdulaziz University were selected for this study to identify and assess the influences of the translated Saudi folk stories in enhancing the reading and speaking skills of Saudi EFL learners. An experiential teaching method using selected translated Saudi stories into English was adopted. The preliminary findings in the pre-intervention stage have shown that the students were neither motivated to read the content nor speak in English as the students encountered some kind of difficulties in comprehending the translated stories. The post-intervention results demonstrated that the students were motivated to read the Arabic stories in English. It also showed that the students were engaged to speak about Saudi / Arabic oral stories and culture. The effect of using Saudi-translated stories was observed in improving the reading and speaking skills of EFL Saudi learners. Thus, the study concludes that it is essential to use the translated Arabic folk stories as supplementary teaching material in the EFL reading and speaking classes. It also suggests that we need to translate Saudi folk stories into English for the benefit of Arabic and non-Arabic readers and speakers.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".