Investigating language assessment literacy: Collaboration between assessment specialists and Canadian university admissions officers
Bibliographic record
Abstract
Abstract There are increasing numbers of non-native English speaking applicants to Canadian universities (AUCC 2008a, 2010), which are committed to promoting linguistic and cultural diversity (AUCC 2008b). One result of this trend is that university admissions officers, as gatekeepers, are faced with a growing and potentially confusing array of language test scores when making their decisions. These admissions decision makers need a certain amount of language assessment literacy (LAL) to enable them to make use of these language test scores effectively and ethically (O’Loughlin 2011, 2013). This article reports on the first phase of a project designed to address this challenge. The project involves the collaboration of assessment professionals and admissions officers across Canada in determining the LAL base needed for users of language test scores in university admissions decision-making. This first phase of research consisted of a survey with university admissions officers across Canada, inquiring about their knowledge, beliefs, and levels of confidence in making use of language test scores in decision-making. Results have begun to reveal the nature of the LAL needed for these users, and have suggested the most appropriate content for later informational workshops with admissions officers (Phase 2 of the project). While some evidence of misunderstanding was identified, respondents demonstrate awareness of concepts related to validity in language assessment, albeit without making use of the conventional language of the field.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".