English for academic purposes in Canada : practitioners’ assessment practices and construction of assessment literacy
Bibliographic record
Abstract
English for Academic Purposes (EAP) continues to expand across post-secondary education settings. In EAP programs, assessment practices play a key role in achieving learning goals and integrating students into relevant academic communities. Research on this critical area, especially in Canada, has been limited. For the most part, neither teacher education nor professional development activities have fully addressed the specialized and interdisciplinary nature of EAP assessment. The present study was designed to explore the issue of the assessment literacy (AL) of EAP practitioners from public and private post-secondary institutions in the Lower Mainland region of British Columbia, Canada. The study investigated the acquisition and development of practitioners’ AL, their self-assessed competence in assessment, engagement in various assessment practices, and assessment practices as members of a peer community in the teaching context. This study was grounded in a theory of learning ecology, which views learning as a socially mediated activity. Participants were EAP practitioners (n=57) representing the diverse population of EAP educators at post-secondary institutions in the region. The study utilized an explanatory sequential mixed methods design, and the data were analyzed to document participants’ roles as assessors and to identify the factors that mediated the development of their AL as well as assessment practices. Findings of the study contribute to the understanding of instructor-oriented EAP assessment in a Canadian context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".