FAIRNESS IN CLASSROOM ASSESSMENT: CONCEPTUAL AND EMPIRICAL INVESTIGATIONS
Bibliographic record
Abstract
This study aimed to investigate fairness in classroom assessment conceptually and empirically to contribute to the emerging research in this area. Conceptually, it critically reviewed the current conceptions of fairness in educational and classroom assessment and enriched these conceptions using a broad review of the key conceptions of fairness and justice in philosophy, sociology, psychology, economics, and education. Empirically, it conducted a two-phase mixed methods study to investigate first year undergraduate students’ perceptions of fairness in classroom assessment in their secondary schools in Ontario, Canada. Phase I included 27 virtual interviews with students that ranged in length from 20-45 minutes and focused on students’ perceptions of un/fairness in classroom assessment. Drawing on the thematic analysis, eight themes were found: (1) overall perceptions of fairness; (2) fairness in groupwork; (3) fairness in exams; (4) fairness in cheating; (5) fairness in grading; (6) fairness in feedback; (7) socio-emotional environment; and (8) responses to perceptions of un/fairness. Overall, these themes coupled with social psychology theory provided the initial foundation to develop the underpinning construct for fairness in classroom assessment. In Phase II, this underpinning construct were used to develop the Classroom Assessment Fairness Inventory. This inventory included demographic questions as well as five scenarios (i.e., groupwork, exam, grading, cheating, and feedback). The logical validity evidence for this inventory was investigated by collecting reviews from 10 international assessment expert panel and 10 graduate students with previous K-12 teaching experience. The empirical validity evidence (i.e., internal structure, and relationship to other variables) was collected by virtually administering the inventory to 217 participants and analyzing the data using factor analyses and multivariate regression analysis. The factor analyses supported a five-factor model including (a) unfairness in groupwork, (b) fairness in cheating, (c) fairness in grading, (d) unfairness in feedback, and (e) fairness in feedback. The multivariate regression analysis showed evidence linking students’ personal belief in a just world with perceptions of fairness in grading (β = .262, p < .05), fairness in feedback (β = .294, p < .05), and unfairness in feedback (β = -.19, p < .05).
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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 | 0.047 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".