Aligning English for Academic Purposes with College Writing Expectations: Insights from One Canadian College
Bibliographic record
Abstract
Canadian colleges and universities vary drastically in the nature of their academic offerings. Universities typically provide academic programs spanning undergraduate, graduate, and professional domains. In contrast, colleges predominately deliver applied and technical programs leading to certificates, diplomas, apprenticeships, and associate degrees, as well as some select undergraduate degrees. While numerous studies have investigated writing requirements at the university level, there is a dearth of research focusing on the Canadian college context. This study aimed to fill this gap by investigating the types of writing assignments commonly used at the college level, as well as the skills necessary to complete these assignments. The ultimate goal was to use the findings to better align our college-based English for academic purposes (EAP) program with the specific writing demands encountered in college programs. Data for this study were collected through two main methods: document analysis and interviews with lecturers. The document analysis entailed a review of course outlines from first-year communication courses across all schools within the college, while the interviews were conducted with seven faculty members who taught these courses. The results of our small-scale qualitative case study demonstrate that the college learning environment requires a distinctive type of writing that integrates both industry-specific and academic elements, a phenomenon we term applied academic writing. This finding not only holds implications for the refinement of the EAP curriculum at our own institution but also holds valuable insights for EAP practitioners in applied learning environments.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".