Academic Writing Challenges and Supports: Perspectives of International Doctoral Students and Their Supervisors
Bibliographic record
Abstract
Introduction Academic writing is a core element of a successful graduate program, especially at the doctoral level. Graduate students are expected to write in a scholarly manner for their thesis and scholarly publications. However, in some cases, limited or no specific training on academic writing is provided to them to do this effectively. As a result, many graduate students, especially those having English as an Additional Language (EAL), face significant challenges in scholarly writing. Further, faculty supervisors often feel burdened by reviewing and editing multiple drafts and find it difficult to help and support EAL students in the process of scientific writing. In this study, we explored academic writing challenges faced by EAL doctoral students and faculty supervisors at a research intensive post-secondary university in Canada. Methods and Analysis We conducted a sequential explanatory mixed-method study using an online survey and subsequent focus group discussions with EAL doctoral students (n = 114) and faculty supervisors (n = 31). A cross-sectional online survey was designed and disseminated to the potential study participants using internal communications systems of the university. The survey was designed using a digital software called Qualtrics™. Following the survey, four focus group discussions (FGDs) were held, two each with two groups of our participants with an aim to achieve data saturation. The FGD guide was informed by the preliminary findings of the survey data. Quantitative data was analyzed using Statistical Package of Social Sciences (SPSS) and qualitative data was managed and analyzed using NVivo. Discussion The study findings suggest that academic writing should be integrated into the formal training of doctoral graduate students from the beginning of the program. Both students and faculty members shared that discipline-specific training is required to ensure success in academic writing, which can be provided in the form of a formal course specifically designed for doctoral students wherein discipline-specific support is provided from faculty supervisors and editing support is provided from English language experts. Ethics and Dissemination The general research ethics board of the university approved the study (#6024751). The findings are disseminated with relevant stakeholders at the university and beyond using scientific presentations and publications.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".