Pedagogical Practices Employed in Teaching and Learning Speaking Skills at Taif University
Bibliographic record
Abstract
The aim of instructing speaking skills is to help students develop better oral communication. That is possible if teachers employ better techniques to enhance the use of English in the class and motivate their learners to utilize important strategies for practicing speaking skills. The current study investigates pedagogical practices in use by English professionals to teach speaking skills to their students, and strategies and approaches applied by leaners to improve their speaking skills. Questionnaire survey was conducted by recruiting 184 male and female undergraduate students from different streams at Taif University. The data was analyzed through SPSS and presented with the help of descriptive tables. The results of this study reveal that teachers moderately employed important techniques and strategies in their teaching. Among the strategies and techniques which were not frequently employed by English professionals were: role-play, group work, pair discussion, picture description, dialogue, debating, and storytelling. Students also lacked in utilizing important strategies which hinder their progress in developing better speaking skills. The attitude of learners towards learning a foreign language was negatively affected by the level of difficulties faced by them. The study concluded that teachers’ role is very important to develop a favorable learning environment and involve students to practice English through various useful techniques and strategies necessary for the development of 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".