An Investigation of Instructors' Approaches in Teaching Pronunciation: A Case Study
Bibliographic record
Abstract
This study aimed to investigate how instructors teach pronunciation based on the pronunciation training they received. This study involved instructors from King Abdul Aziz University, who were teaching at the English Language Institute (ELI). The data were collected through a questionnaire given to (50) instructors at (ELI). The data were analyzed using Statistical Package for the Social Sciences (SPSS) program. The results displayed that instructors used cognitive-content of teaching pronunciation and most of them focused on it as a valuable teaching approach. Furthermore, the findings revealed that the instructors had a constructive trend in teaching pronunciation. Most instructors pointed out that they taught pronunciation in their classes; in many cases they spent a considerable amount of time in pronunciation instruction. The lack of the pronunciation equipment and technological resources stands as a stumbling- block problem to teaching this language skill. In addition, the findings showed most of the language instructors did not receive any specific pronunciation training. Recommendations are given to provide suitable teaching pronunciation training which prepares the instructors to use powerful technology to boost teaching of this essential skill.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".