Language Assessment Literacy as Professional Competence: The Case of Canadian Admissions Decision Makers
Bibliographic record
Abstract
Abstract This language assessment literacy project involves the collaboration of assessment professionals and admissions officers at higher education institutions across Canada. Following a survey with 20 institutions (Baker, Tsushima, & Wang, 2014), workshops were held at eight institutions across the country dealing with the use of language test scores in university admissions decision making. Recordings of these workshops were analyzed within the typology of workplace knowledge developed by Eraut and his colleagues (Eraut 1994, 2000, 2004a, 2004b; McKee & Eraut, 2012). In addition, participants commented on the usefulness of the workshop materials for their work responsibilities. Results provided insights into a) the language assessment literacy (LAL) base needed for these specific users, including both propositional and procedural components, and b) the possibilities of conceptualizing LAL for these score users as a type of professional workplace competence. Résumé Ce projet de sensibilisation en évaluation linguistique a été effectué avec la collaboration de professionnels de l’évaluation et de responsables des admissions dans les établissements d’enseignement supérieur à travers le Canada. Suite à un sondage effectué auprès de 20 institutions (Baker, Tsushima et Wang, 2014), des ateliers ont été organisés dans huit établissements à travers le pays traitant de l’utilisation des résultats des tests de langue dans la prise des décisions d’admission. Les enregistrements de ces ateliers ont été analysés dans la typologie des connaissances en milieu de travail développé par Eraut et ses collègues (Eraut, 1994, 2000, 2004a, 2004b ; McKee et Eraut, 2012). Les participants ont formulé des observations sur l’utilité du matériel d’atelier pour leurs responsabilités professionnelles. Les résultats nous aident à mieux comprendre a) les notions en évaluation de langues les plus pertinentes pour ces utilisateurs spécifiques et b) le potentiel de concevoir la « littératie en évaluation » comme étant un type de compétence professionnelle en milieu de travail.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.017 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".