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Record W4411475738 · doi:10.25071/1916-4467.40823

High-Stakes Tests and Applied Learners: The (Dis)connections Between Curriculum Expectations and Exam Notions of “Literacy”

2025· article· en· W4411475738 on OpenAlexaffvenueabout
Claire Ahn, Nathan Rickey, Alexandra Minuk, Jane Chin, Rebecca Luce‐Kapler

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsCurriculumLiteracyTest (biology)Mathematics educationPedagogyPsychologyThematic analysisSociologyQualitative research

Abstract

fetched live from OpenAlex

In 2019, 59% of applied English learners failed the Ontario Secondary School Literacy Test (OSSLT), which is a requirement to graduate high school in Ontario. The authors of this paper wondered about the 41% who passed. We asked: What curricular connections are being made (or not made)? What is working well? With these questions in mind, the purpose of this article is to share findings from a thematic analysis of literature focusing on applied learners and the OSSLT. Discussions also include findings from a survey that shares the perspectives and experiences of English educators who support students in their applied classrooms on the OSSLT. Findings show a disconnect between curricular and OSSLT assessment expectations of what is considered and valued as literacy. This article highlights a greater need to find and develop best practices for teaching learners in applied English classrooms and for sharing these evidence-based strategies. Such best practices can help educators further support students in applied English classroom to better prepare for the OSSLT which might also inform curriculum development, literacy instruction and standardized testing.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.339
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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