An Analysis of Afghan Students Challenges in Academic Writing in US Universities
Bibliographic record
Abstract
For more than 35 years, there is no research done to find out the writing challenges of this specific Afghan students in the US.Thus, the present research undertakes a study of the qualitative analysis of the challenges that Afghan graduate students have in English academic writing across disciplines.In particular, it addresses the specific challenges that students are facing in regard to academic writing and the impact of these challenges on their class performance.It also shed lights on the factors behind those challenges and students' perceptions of what they need to overcome them.For this study, the data were collected through survey and interviews with Afghan graduate students from different disciplines in US universities.The results of the study indicated that Afghan students face challenges in all aspects of English academic writing.Nevertheless, paraphrasing, citing sources, generating ideas, and writing for different audiences are the most challenging skills.These challenges stem from a lack of focus on academic writing curricula both in English and in the native language, an overall unfavorable system of education, and cultural and linguistic backgrounds.Hence, this study suggests a change in the curricula from a lack of focus to a strong focus on academic writing, establishment of writing centers, provision of more writing resources in US universities, and overall changes in the current system of education in Afghanistan.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".