Challenges Faced by Bachelor Level Students While Speaking English
Bibliographic record
Abstract
Speaking is regarded as an indicator of language proficiency in general. It is believed that a learner who can speak a particular language fluently is regarded as a proficient learner of that language. In the context of Nepal, the English language is taken as a foreign language and taught from elementary level to university level as a compulsory subject but Nepalese students in general and university students in particular, face a lot of difficulties in speaking English fluently. Even after completing their graduate degree, some of them could not speak a little bit of English. This present study tried to explore the difficulties faced by undergraduate level students and the possible causes of their difficulties in speaking skills. This is an empirical qualitative study in which the researcher adopted a questionnaire and semi-structured interview to collect data from 15 undergraduate level students studying at the University. The collected data were thematized and analyzed in terms of two broad categories: Difficulties and causes with four/four subcategories of the broad themes. The study explored mainly: personal, social, environmental, and linguistic problems for speaking difficulties and teacher and teaching, course content, overuse of mother tongue, poor schooling, and classroom culture as the causal factors of speaking deficiency. The study suggested creating a favorable environment, maximizing learner autonomy, changing teaching practices, revising courses, and conducting speaking activities time and again.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".