Teachers’ Experiential Strategies for Bridging Writing Proficiency Gaps among Secondary Level Students
Bibliographic record
Abstract
The secondary level English curriculum of Nepal aims to enable students to produce various forms of writing items, such as paragraphs, essays, reviews, stories, dialogues, and letters in both personal and academic contexts. However, many students in Nepalese context struggle in the early stages of writing proficiency due to which they are unable to accomplish the competency as targeted by the curriculum. Therefore, this study intends to explore the strategies to bridge the gaps between target of curriculum and proficiency level of students in Nepalese ELT classes by collecting data from secondary level English language teachers through in-depth interview under the framework of hermetic phenomenology and interpreting their lived experiences creating the themes on the basis of broader and specific meaning units. I found that the ecosystem in language teaching created the atmosphere to learn language skills and aspects interactively. Additionally, the theme and genre-based writing activities enabled learners to develop writing skill with proper style and cohesion. This study will be beneficial for those English language teachers who are facing problems of writing proficiency gaps in their students while developing writing skill. With the help of strategies suggested by this study, the teachers can guide students to develop both initially fundamental writing skills and then gradually take them to the level of creative writings to meet the target of curriculum.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".