Anxiety in EFL Classrooms: Impact on Linguistic Proficiency and Skill Development
Bibliographic record
Abstract
English is a vital medium for education and communication in Nepal, making its acquisition crucial for academic and professional success. However, the process of learning English as a foreign language (EFL) often induces significant anxiety among students, which can hinder their language development and overall performance. This study explores the anxiety levels of secondary-level EFL students in Nepal as they acquire language skills in EFL classrooms. Sixty students from three community schools were selected, and data were collected using a Google Forms questionnaire based on the descriptive approach. The findings reveal varying levels of anxiety across different language skills, with speaking skills eliciting the highest anxiety, followed by writing, listening, and reading skills, which caused moderate anxiety. Contextual disparities were also noted, with test and exam-related anxiety being the most significant. Additionally, nervousness and students' proficiency levels emerged as primary contributors to their anxiety. The study highlights the necessity of implementing comprehensive, context-specific strategies in English language classrooms to address the diverse and intense forms of student anxiety, ultimately promoting more effective language learning and the need for targeted interventions in EFL classrooms to reduce anxiety and enhance students' language learning outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".