Preservice teachers' assessment decisions: Exploring the role of fairness conceptions
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
Abstract Teachers' conceptions of fairness influence their approaches to assessment. Perceptions of fairness underpin how teachers legitimate their values and provide reasons for teachers to defend assessment decisions and actions. This study therefore examined fairness conceptions at a critical stage in teachers' journey towards assessment capacity: their perceptions as teacher candidates. Specifically, we investigated 228 preservice teachers' conceptions of assessment fairness using the Classroom Assessment Fairness Inventory (CAFI). The results of an exploratory factor analysis (EFA) showed four factors: assessment communication acts , grading decisions , responses to cheating and firm assessment decisions . These factors highlighted the domains and underpinning principles of fairness that participating preservice teachers used to evaluate issues of fairness in assessment. The results provide initial empirical foundations to promote nuanced understandings of fairness in assessment education.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it