Classroom assessment fairness inventory: a new instrument to support perceived fairness in classroom assessment
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
Fair assessment in the classroom is a concern from student, teacher, principal and public perspectives. Standards and policies also underscore fairness as a key underpinning for assessments. Perceptions of fairness impact students’ socio-emotional and learning outcomes, and build confidence, trust and legitimacy for the assessment outcomes. However, a direct inquiry into students’ perceptions of what a fair assessment means has received scant attention. Without appreciating students’ perceptions, we lack insights into how students’ perceptions of fairness impact their responses to assessment outcomes for learning and grading purposes. Therefore, this study leveraged the Classroom Assessment Fairness Inventory to investigate first-year undergraduate students’ perceptions of fairness about their secondary school experiences in Ontario, Canada. The results present initial validity evidence for the inventory to support a fairness theory and practice in classroom assessment contexts.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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