Faculty instructors’ perceptions of and support for international students' academic writing
Bibliographic record
Abstract
With the increasing enrollment of international students in Canadian higher education, international students’ life and academic experience has become an important consideration. Most studies attempt to determine the discrepancy between the requirements of English-speaking institutions and international students’ perspectives or behaviors (Hughes, 2013; Rienties et al., 2012; Singh, 2017). Especially in academic writing, previous research (Newton & McCunn, 2015; Neumann, Padden & McDonough, 2019) emphasized the importance of modifying students’ writing performances in order to better adapt to the new academic environment. However, what writing support that international students can receive also plays an important role to help improve their writing abilities (Sharma, 2018). Even though specific writing courses or service offered by English-speaking institutions can address students’ writing techniques, they cannot help students acquire disciplinary conventions and employ those conventions effectively in academic discourse (Leki & Carson,1997; Wingate, 2018). Therefore, the support from faculty instructors who teach students’ academic courses is crucial for international students’ success. However, there has been little research investigating the writing support instructors provide for their international students. Thus, this study which deploys focus groups and a survey, is a mixed methods approach to investigate, firstly, the faculty instructors’ (N = 67) perceptions of international students’ writing performance in Canadian universities and, secondly, what they do to support these students’ writing. This study identifies the challenges that instructors face, such as a heavy workload and a lack of sufficient time, when providing academic writing support, and thus suggests that institutions should offer appropriate and sufficient support for their instructors
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".