Pronunciation Errors of Letter “G” in English Language Made by Saudi Undergraduate Students
Bibliographic record
Abstract
The primary goal of the present study is to identify the problematic areas in the pronunciation of the letter “g” in English written words made by Saudi female learners of English as a foreign language, and the reasons for the weakness associated with mispronunciation of English written words which contain this letter. The population of the study was the female students (90 students) and their English language teachers (12 teachers) at the Qassim University during the academic year (2014-2015). There were two types of instruments used in this study. The first was a pronunciation test for the student participants in order to investigate the problematic areas of pronouncing “g” in different environments in different words; and the second a questionnaire for the teacher participants to provide comprehensive data about the causes of these errors of pronouncing “g” committed by EFL female students at Qassim University. Ninety female students were included for the pronunciation test and 12 teachers were asked to answer the questionnaire. Simple percentage was used for analyzing the data of recording words (pronunciation test). Results of the students’ recording words revealed that the participants mispronounced “g” before nasals (68%). According to the results of the teachers’ responses to the questionnaire suggested many factors that can cause difficulties for students in terms of pronouncing “g” in English written words. According to them, these difficulties are concerned with reading difficulties, nonstandard spellings, letters that follow “g” (many of them may become combinations), loan words, orthography (no correspondence between the English alphabets and their sounds). The researcher offers recommendations that might help teachers and students to overcome and reduce these mispronunciations of this letter in English written words.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".