The Teaching of English Pronunciation: Perceptions of Indonesian School Teachers and University Students
Bibliographic record
Abstract
This study aimed to explore teachers’ and students’ perception of pronunciation teaching in Indonesian EFL classrooms, particularly on (1) the difficulty of English pronunciation, (2) the reasons for the difficulty, (3) the inclusion of pronunciation in EFL classrooms, (4) the goal of pronunciation teaching, (5) priorities in pronunciation teaching, and (6) techniques in pronunciation teaching. To achieve the purpose, a written questionnaire was distributed to 110 Indonesian school teachers and 230 Indonesian university students. The collected data were submitted to independent two-sample t test to determine the significant mean differences between the teacher and student participants. The results of the study discovered that almost all of the respondents perceived English pronunciation as one of the most difficult areas in English learning. The participants also agreed that two most significant reasons for the difficultly were related to students’ first language (L1). Regarding these results, the participants strongly agreed on the inclusion of pronunciation in EFL classrooms with intelligibility as the goal of pronunciation teaching. Moreover, segmental features such as consonants and vowels as well as sentence stress became the priorities in pronunciation teaching for EFL learners. Finally, teacher explanation in students’ L1, followed by demonstration of how to produce the English phonemes, is significantly rated as the better way to teach English pronunciation in EFL classrooms. The finding of the study implies that intelligibility as the goal of pronunciation teaching can be really attained with the consideration of priorities and techniques in pronunciation teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".