EFL Learners’ Attitude towards Developing Speaking Skills at the University of Taif, Saudi Arabia
Bibliographic record
Abstract
Speaking is the most difficult as well as the most complex of all the four skills, as it requires expertise in, and exposure to, the target language. Different factors are found responsible for poor speaking skills among EFL learners in general and Saudi EFL learners in particular. The current study investigates the influence of various factors related to teachers, learners, and learning environment on the students’ attitude towards learning speaking skills. The questionnaire survey was employed to elicit responses from 184 undergraduate EFL male and female students in Taif University. Data analyzed through SPSS reveals that out of five variables only one was insignificant, whereas all other variables showed significant positive effect. In the light of the findings, it could be inferred that lack of measures on the part of teachers and learners as well as the classroom setting/environment do not fully facilitate both the male and female students to learn speaking skills in a better way. The poor level of their skills in English is attributed to the variety of teachers’, learners’, and environment related factors. And these factors affect negatively on the attitude of learners towards learning speaking skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".