Academic Writing Challenges and Encouragements: Perspectives of University Teachers in Far Western University
Bibliographic record
Abstract
While academic writing skills constitute a central place in master’s level courses at universities, institutional support for students has often been lacking. As a result, students face challenges in producing scholarly writing. In this study, I attempted to explore the academic writing challenges and encouragements to enhancing academic writing of master’s level students from the perspectives of university teachers at Far Western University of Nepal. This is a qualitative study. The data were collected through semi-structured interviews. Five university teachers from three different disciplines were selected as the research participants. The study shows that students’ awareness of academic writing is very low. Additionally, the traditional role of supervisors has negative effects on students’ academic writing. The study, however, reveals that training, workshops, virtual seminars and individual feedback have contributed to improving their academic writing. The study concludes that there is no adequate provision for research, and the university does not seem to have visible policies and plans for developing students’ academic writing.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".