‘There was very little room for me to be me’: the lived tensions between assessment standardisation and student diversity
Bibliographic record
Abstract
Higher education aims to educate diverse professionals to operate in an increasingly complex world. Yet, academic assessment practices still rely upon standardisation, namely, that all students should demonstrate their achievement in ways that are largely comparable, if not identical. In this study, we theorise assessment standardisation as a technology of normalisation upon student diversity and identities. Our study is located in one of the most complex learning settings in higher education: placements. We theorise how diverse students navigate the tensions arising from standardised assessment situations that assess highly personalised forms of learning in complex assessment settings. Our data material consists of longitudinal interviews with 16 disabled university students in Australia before, during, and after a placement. Our findings show that assessment suppresses and normalises students’ diverse identities, calling into question the inclusivity of such assessment practices. We discuss how assessment provides students with narrow ways of forming their professional identities. While this is the case for all students, the social consequences of assessment standardisation might be more crucial for those who do not fit the ‘norm’ set by assessment, such as disabled students in our case.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.048 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| 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".