Conceptualising a Fairness Framework for Assessment Adjusted Practices for Students with Disability: An Empirical Study
Bibliographic record
Abstract
Given the increasing diversity of teachers and students in 21st century classrooms, fairness is a key consideration in classroom adjusted assessment and instructional practices for students with disability. Despite its significance, little research has attempted to explicitly conceptualise fairness for classroom assessment adjusted practices. The purpose of this study is to leverage the multiple perspectives of secondary school students with disability, their teachers, and parents to build a multi-dimensional framework of fairness for assessment adjusted practices. Open-ended survey data were collected from 60 students with disability, 45 teachers, and 58 parents in four states in Australia and were analyzed using qualitative inductive analysis. The findings present a multidimensional framework for assessment adjusted practices that include interactions across elements of assessment practices, socio-emotional environment, overall conceptions of fairness, and contextual barriers and facilitators. The interactions across these elements influence the learning opportunities and academic outcomes for students with disability.
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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 | 0.055 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".