Grammatical Errors in Nigerian English Language Pronunciation Problems Among Students: Psychological Implications and Management
Bibliographic record
Abstract
This study was designed to identify some English grammar pronunciation problems and how the problems can be managed. The population consists of all the first-year students in the University of Nigeria, Nsukka in Enugu State of Nigeria. Nine hundred and ninety-nine (999) students from four departments were sampled using simple random sampling technique. Three research questions and one null hypothesis were generated to guide the study. Mean and standard deviation were used to answer the three research questions while t-test statistic was used to test the null hypothesis. The results revealed that consonant and vowel phonemes, syllabic consonants, consonant cluster, unstressed vowels and stress timing are the aspects of pronunciation that are considered problematic among some first-year students. It also revealed that gender does not play a functional role on pronunciation problems among these first year students in the learning of English Grammar, and that teachers’ emphasis on the problematic area of pronunciation, constant practice, teachers’ knowledge of Oral English, building of language laboratories, teachers’ use of compact disks and tapes on pronunciation, students access to compact disks and tapes on pronunciation dictionaries are the management strategies that can be used to improve pronunciation in English language.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".