Exploring the Writing Process of Multilingual Postsecondary Students
Bibliographic record
Abstract
With an increasingly multilingual population made up of domestic and international students at Canadian universities, there is a knowledge gap about the writing practices of multilingual students and the needs of multilingual academic writers. In order to address this knowledge gap, more research is required about the writing process of multilingual postsecondary students in Canada. The purpose of this study was to learn in detail about the writing process of multilingual postsecondary students in a mid-sized university in eastern Canada. A qualitative methodology consisting of semi-structured interviews was followed. A small sample size of seven participants consisted of young adults enrolled at the bachelors or graduate level who were recruited through posters on campus. The interviews were transcribed, coded holistically, and thematically analyzed using software. Themes reveal the writing process, prescriptive instruction and adherence to rules, planning prior to writing, prior knowledge of academic writing, and experience versus inexperience in writing. The meta-themes were continua of agency/following instructions, experience/inexperience, and explicit teaching/finding their own methods Secondary findings highlight the impact of instructor feedback on learner attitudes and English language learners’ need for extra time to develop their academic English. Additional findings show that multilingual postsecondary students use translanguaging as a strategic tool when composing in English. These findings offer insights into the translingual writing process of multilingual postsecondary students.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".