The Prominent Barriers to Speaking in English: A Study Conducted Among Youngsters
Bibliographic record
Abstract
This discourse analyses the prominent barriers to speaking in English while conducting online English Language classes during the pandemic, COVID-19. The study is conducted among business communication students in university colleges in India and takes five paradigms into consideration. They are: the motivational factor, the personality of the learner, attitude of the learner, the pedagogical management of English classes in online mode and the level of exposure to the English language. Data were collected by analyzing the survey questionnaire distributed among 150 business communication students. Data were analyzed with the help of SPSS in a descriptive mode. The result of the analysis shows that while dealing with online classes, teachers face several difficulties to manage the language subjects, especially the pedagogical management of the English subject. Another significant factor is the level of exposure to the English language. In this online system, ordinary students do not have an opportunity to communicate and practice English. They show some kind of hesitation to use English during the entire class time and give less attention to the words of the teacher. Most of them are distracted due to several factors. It contributes moderately to the predicaments of the learners. This study also helps to understand the crucial factors that act as language barriers in cross cultural business communication as the application level of language is more or less same all over the world.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".