Teaching English in Diverse Settings: Lived Experiences of Nepalese ELT Teachers
Bibliographic record
Abstract
This study explores the lived experiences of English Language Teachers in Nepal’s rural, multilingual, and multicultural contexts, focusing on the Darchula district. Adopting a hermeneutic phenomenological approach, the research examines how four secondary-level ELT teachers navigate the pedagogical complexities of multilingual classrooms. Data were collected through semi-structured interviews and classroom observations, and interpreted using thematic analysis. The study is informed by Translanguaging Theory and Vygotsky’s sociocultural perspective, highlighting how teachers draw on students’ linguistic and cultural backgrounds to support learning. t i Findings indicate that multilingual classrooms foster met linguistic awareness, empathy, and cultural sensitivity, enabling teachers to develop adaptive and inclusive teaching strategies. Translanguaging emerged as a key practice to bridge linguistic gaps, enhance participation, and deepen comprehension. Culturally responsive teaching also played a vital role in affirming students’ identities and creating equitable learning spaces. Despite these strengths, teachers reported challenges, including insufficient training in multilingual pedagogies, limited institutional support, language hierarchies, and resource constraints. Participants emphasized the need for professional development that is contextually relevant and addresses both the pedagogical and emotional demands of teaching in diverse settings. This study calls for the integration of multilingual awareness, translanguaging practices, and cultural competence into teacher education in Nepal. By foregrounding rural teachers’ voices, it contributes to the broader discourse on equitable and inclusive language education and offers insights for policy and professional development aimed at enhancing teaching practices in linguistically complex classrooms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".