Contribution of International Students’ Perceptions to the Canadian Academic English Language Test
Bibliographic record
Abstract
This study aims to examine international students’ perception of English language abilities in use in Canadian university academic context. By drawing on the perceptions of current international students who regularly meet the actual academic demands in the real-world university setting, this study will inform the Canadian Academic English Language – Computer Edition (CAEL CE) about the relevance of the academic language task and skills in its test construct to the target language use (TLU) domain. The international students in this study will be first-year undergraduate students (n=300) among whom there will be Canadian Academic English Language – Computer Edition (CAEL CE) past test-takers (n=5-10). In this study, the constructs of academic English language abilities (i.e. listening, speaking, reading and writing skills) will be examined by analyzing the academic tasks operational in the Canadian university classrooms and the language skills required to fulfill those tasks. Following the socio-cognitive model for language test development and validation (Weir, 2005a) this will be a mixed-methods study employing a two-phase sequential explanatory design with two phases: Phase One (quantitative) followed by Phase Two (qualitative). Both phases will answer all the research questions adding to CAEL CE's validity evidence.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".