Untangling the Tongues: Exploring Pronunciation Development Strategies for Learners of English at High Schools in Oromia Region of Ethiopia
Bibliographic record
Abstract
One of the most important aspects of learning a new language is mastering the pronunciation of that language. It is crucial in helping second language learners improve their communication skills and overall performance. Mastering proper pronunciation is a difficult and subtle part of teaching English as a foreign or second language. The focus of this study is to assess the contributing factors affecting ESL students' pronunciation. The study used a quantitative research approach. A total of 189 students from Burayu High School were chosen at random sampling technique for this study. The researchers collected data through a questionnaire. SPSS version 26 was used to analyze the data obtained from the respondents of the survey. Factors influencing pronunciation that were identified in this study include poor motivation, lack of confidence, exposure, practice, interest, an unfriendly atmosphere, one's native language interference, age, personal traits of the learners, instructional approach, and feedback deficiency. The findings also provide valuable insights into preferred pronunciation learning strategies among students of English in the Oromia region of Ethiopia and explore the impact of individual and environmental factors, including first language influence, strengths, and weaknesses of current teaching methodologies in fostering pronunciation development in this specific context. This study will contribute to the development of more effective and learner-centered pedagogical approaches for pronunciation instructions in English language programs designed for English learners in the Oromia region of Ethiopia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".