English Academic Writing and Affordances for Writing Support: Perspectives and Experiences of Plurilingual International Course-based Master of Education Students in a Canadian University
Bibliographic record
Abstract
Many international students are pursuing graduate education in Canada using languages other than English. The Master of Education degree has been particularly attractive as it offers significant cultural capital (Bourdieu, 1986) in return for a relatively short period of study. However, there is an implicit expectation that because these students have met admission standards, they should be performing at the same level as their peers who use standard English. Should these international students face challenges with their written English proficiency, they experience discourses of deficit that negatively affect their experience in the academy. Using a multiple case study design, I conducted a three-part series of semi-structured interviews with plurilingual international course-based Master of Education (PIM) students at the Ontario Institute for Studies in Education (OISE) to examine their perspectives on and experiences with English academic writing and academic writing support. To learn about the affordances for academic writing support for these students, I collected data from a variety of sources, including a demographic questionnaire, participant observations, document analysis, focus group with students, and interviews with support providers. I examined the resulting data using Framework Analysis (Spencer et al., 2014) and the theoretical lenses of Academic Literacies (Lea & Street, 1998, 2006) and ecological perspectives (Bronfenbrenner, 1997; Bronfenbrenner & Morris, 2006; Van Lier, 2000, 2004). The PIM students in this study reported challenges with English academic writing at the graduate level that left them feeling anxious and inadequate. To resist being marginalized by an English-dominant ideology that adopts a deficit view rather than values the diverse assets afforded by plurilingualism, these students demonstrated agency by engaging with an array of affordances that served to build English writing skills, to socialize students into the discourse practices of their disciplinary communities, and to develop identities as novice English scholarly writers. This study builds upon the literature that views academic writing support as integral to all students’ development as academic writers while recognizing the unique needs of plurilingual international graduate students. Implications from this study suggest a need for culturally and linguistically responsive pedagogy, and policies that help students leverage their plurilingual repertoire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".