Toward a Differential and Situated View of Assessment Literacy: Studying Teachers' Responses to Classroom Assessment Scenarios
Bibliographic record
Abstract
Research has consistently demonstrated that teachers’ assessment actions have a significant influence on students’ learning experience and achievement. While much of the assessment research to date has investigated teachers’ understandings of assessment purposes, their developing assessment literacy, or specific classroom assessment practices, few studies have explored teachers’ differential responses to specific and common classroom assessment scenarios. Drawing on a contemporary view of assessment literacy, and providing empirical evidence for assessment literacy as a differential and situated professional competency, the purpose of this study is to explore teachers’ approaches to assessment more closely by examining their differential responses to common classroom assessment scenarios. By drawing on data from 453 beginning teachers who were asked to consider their teaching context and identify their likely actions in response to common assessment scenarios, this paper makes a case for a situated and contextualized view of assessment work, providing an empirically-informed basis for reconceptualizing assessment literacy as negotiated, situated, and differential across teachers, scenarios, and contexts. Data from survey that presents teachers with assessment scenarios are analyzed through descriptive statistics and significance testing to observe similarities and differences by scenario and by participants' teaching division (i.e., elementary and secondary). The paper concludes by considering implications for assessment literacy theory and future related research.
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".