From challenge to innovation: A grassroots study of teachers’ classroom assessment innovations
Bibliographic record
Abstract
Abstract We are at a critical moment for assessment in schools. Teachers are called to navigate advances in classroom assessment research, top‐down assessment policies, and lingering effects of the COVID‐19 pandemic on teaching and learning. Embedded in this context are also systemic challenges to teachers’ assessment practice. This paper analyses these challenges to characterise the current context for teachers’ assessment work and considers teachers’ innovative responses to these challenges. Data are drawn from 168 qualitative responses to a baseline assessment innovation survey across 10 Canadian provinces and territories as well as 10 other international jurisdictions. Eight themes were identified related to teachers’ assessment challenges and innovations, including: negating innovation, the emotions of assessment, grade obsession and the gradeless spectrum, conflicting orientations towards assessment, the use of ‘assessment talk’, data overload, equitable assessment and actions that make learning and assessment visible. These findings directly support the widespread goal of implementing assessments that effectively and consistently serve student learning. The paper concludes with a discussion on how teachers move from facing assessment challenges to engaging in assessment innovations.
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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".