Using the First Language (L1) as a Resource in EFL Classrooms: Nepalese University Teachers’ and Students’ Perspectives
Bibliographic record
Abstract
While challenging the widely held belief that students in English as a foreign language (EFL) classroom prefer their teachers not use the first language (L1), the study examined attitudes of university teachers and students towards using L1 and reasons for giving up on English and reverting to Nepali in English-medium lessons. Drawing on a mixed-method study that used survey questionnaire (N= 50) and interviews (N=15), the researcher identified a number of classroom speech acts that are performed by teachers’ and students’ in their L1. The findings revealed that both teachers and students had a positive attitude towards using L1; however, they held the belief that the overuse of L1 may impede language learning. Although the teachers seemed to discourage the use of Nepali (L1) in lessons aimed at developing learners’ communicative competence, they used Nepali to help learners comprehend complex concepts of grammar and lexis. Although the excessive use of Nepali was seemingly associated with teachers’ lack of communicative competence and creativity in delivering EFL lessons, students preferred their teachers to use the L1.Journal of NELTA , Vol. 22, No. 1-2, 2017 December
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".