Bibliographic record
Abstract
Consequently, the present study sheds light on a very important aspect that is a contrastive analysis of segmental vowel phonemes of both L1 and L2. As one of the Problems, that is affecting the teaching/learning process of ELT. Then to clarify the different areas between the segmental vowel phonemes of Arabic and English. It also aims at making a comparative segmental analysis in the vowel phonemes of both L1 - L2, in order to shed light on the areas of difficulties. Taking into account the different forms of sounds in relation to their spellings. Also the sound systems of both languages L1 and L2. Particular the areas of common mistakes. Thus, it encourages teachers to check English pronunciation before teaching and predict problems before they happen. Also the use of advanced methods and pronunciation dictionary for (IPA). It also helps learners to master all the significant sound features and basic structural patterns. On the phonological levels, differences cause difficulties e.g. The areas of Arabic /p/ and /v/ ,and English front vowels phonemes /i:/-/i/- /e/- /æ/, central /ə:/- /ə/-/Λ/,and back/a:/-/o/-/ ﬤ:/-/u/-/u:/ phonemes in English, they do not exist in Arabic. In addition to the English vowel /e/ which doesn’t exist in Arabic. This observation can't be only linguistically, but it will also confirm by L2 learners. These theories need to be clarified in order to allow rules to be expressed. In the English language, there are 26 alphabetical letters which are totally different than their sounds. Another drawback is the alphabetical method which is intended to teach reading by means of spelling. There is no one –to- one correspondence between sounds and letters. That is to say, each different sound cannot be represented by the same letter. There are also words which are spelt differently but sound the same. Therefore, the present study concentrates on the difficulties that Learners and beginners face in using English segmental vowel phonemes. It also tries to get new methods and new ideas. This study is based on the practical experience of the writer, being specialized in English phonology, a lecturer and an author of a phonetics textbook for beginner learners who learn English as a foreign language. And as a supervisor who follows undergraduate students in the field of experience. That is, in order to find out possible remedial solutions, better suggestion and recommendations. Then follows a descriptive method to achieve this purpose. With the sample of twenty student girls. As a case study of undergraduate trainees of Majmaah Universitiy in the training field, to collect data from the subjects’ real environment during talks, speeches, presentations and teaching in the Field Experience. The results were recorded to be analyzed. In particular, the areas of English vowels and diphthongs that are totally different than in Arabic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".