High-Stakes Tests and Applied Learners: The (Dis)connections Between Curriculum Expectations and Exam Notions of “Literacy”
Bibliographic record
Abstract
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 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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".