Exploring assessment across cultures: Teachers’ approaches to assessment in the U.S., China, and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Classroom assessment dynamics are shaped by individual and local understandings of assessment (assessment micro-cultures), as well as common assessment beliefs and practices that stem from system-wide features, such as large-scale testing (assessment macro-cultures). Teachers’ approaches to assessment reveal how they navigate assessment micro- and macro-cultures to support student learning and achievement. Despite increasing migration of students between the U.S., China, and Canada, little research has examined the different approaches to assessment students encounter when they move between these contexts. Thus, the specific supports they need to adapt to their new assessment cultures and have equitable access to learning have remained unclear. This exploratory research compared teachers’ approaches to assessment in the U.S., China, and Canada. Latent class analysis identified five types of assessors across these contexts: teacher-centric assessors, hesitant assessors, moderately student-centric assessors, highly student-centric assessors, and eager assessors. Associations between assessor type and country were identified, revealing different patterns in how teachers approach assessment in each education context.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it