“Who Bothers!” A Common Ailment in Higher Education ELT Classrooms in Nepal
Bibliographic record
Abstract
Critics of higher education in Nepal, even the concerned agencies, are much worried about the decreased quality resulting in low employability of higher education. The most common adjectives used by them to describe this state are ‘theoretical’ and/or ‘impractical’. The present case study was instigated when two college students of third year Bachelor’s degree majoring in English, one from Bachelor of Arts (B. A.) and the other from Bachelor of Education (B. Ed.), remarked that the excessive lecture-based classes at college were not worth attending regularly. Stemming from this problem, each of the classes was observed once to see if the students’ remark would be verified. As a triangulation process, the observation was then followed by an informal post-class interaction with the faculties whose classes were observed. This article, thus, basically assesses the efficacy of the excessive lecture (EL) within the limitation of teaching English to adult learners of higher education in Nepal. Considering the inefficacious nature of EL to cause learning, and the faculties’ (Note 1) perceptions towards, and an over-attachment with, this method as the unique one, some alternative strategies applicable to English language teaching (ELT) classes have been recommended with the hope that they would be properly used to keep the burdened use of EL reasonably low. The article also recommends some changing roles of faculties involved in ELT in the higher education sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".