An Analysis of Large-Scale Writing Assessments in Canada (Grades 5-8)
Bibliographic record
Abstract
This paper reports on an analysis of large-scale assessments of Grades 5-8 students’ writing across 10 provinces and 2 territories in Canada. Theory, classroom practice, and the contributions and constraints of large-scale writing assessment are brought together with a focus on Grades 5-8 writing in order to provide both a broad view of Canada-wide assessments and specific recommendations for enhancing the validity of provincial and territorial writing assessment in Canada. We deductively analyzed the primary assessment administration documents found on the provincial and territorial education websites using the categories of (a) design (e.g., grades at which the tests are written, the goals of the tests, the number and types of written compositions that are gathered, (b) administration (e.g., time of year, length of time provided to students to write, and pre-writing activities), and (c) the scoring of the assessments. We also used tenets of effective writing assessment from a process writing approach and from a multiliteracies approach to analyze the assessment procedures. Our analysis shows that process writing approaches have influenced the administration procedures in terms of the provision of time to talk with peers before writing and the recognition of various composition and thinking processes. However, composing processes are directed to be less idiosyncratic and recursive than composition theorists and noted teachers of writing would recommend. The assessments do not yet reflect an awareness of multiliteracies theory, as there is little use of digital technology to write and portfolio assessments and the collaborative writing of Web 2.0 practices are non-existent. Cet article fait état d’une analyse d’évaluations à grande échelle de rédactions d’élèves de la 5e à la 8e année dans 10 provinces et 2 territoires au Canada. Se penchant sur les rédactions écrites par des élèves de la 5e à la 8e année, nous réunissons la théorie, la pratique en salle de classe et les avantages et les contraintes liés aux évaluations à grande échelle pour élaborer un aperçu général des évaluations pancanadiennes et des recommandations spécifiques visant l’augmentation de la validité des évaluations de l’écriture dans les provinces et les territoires du Canada. Nous avons analysé par déduction les documents administratifs de l’évaluation principale tirés des sites Web d’éducation des provinces et des territoires. Notre analyse reposait sur les trois catégories suivantes : (a) conception (p. ex. niveau scolaire auquel on écrit les examens, objectifs des examens, nombre et type de rédactions exigées), (b) administration (p.ex. période de l’année, temps de rédaction accordée aux élèves, activités de pré-écriture et (c) évaluation des examens. Nous nous sommes également appuyés sur les principes de l’évaluation efficace des rédactions selon une approche processus et une approche axée sur la multilittératie pour analyser les procédures d’évaluation. Notre analyse démontre que les approches processus à l’écriture ont influencé les procédures administratives quant au temps accordé à la discussion avec les pairs avant l’écriture et quant à la reconnaissance de divers processus de rédaction et de réflexion. Toutefois, les processus d’écriture imposés sont moins idiosyncratiques et récursifs que des théoriciens de l’écriture et des enseignants éminents de la rédaction recommanderaient. Les évaluations ne reflètent pas encore une prise de conscience de la théorie de la multilittératie, l’emploi de la technologie numérique pour écrire étant très limité, et les évaluations de portfolios et l’écriture coopérative qui découle des pratiques de Web 2.0 étant inexistantes.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".