A students-as-partners-inspired approach to assessment rubric design
Bibliographic record
Abstract
The global popularity of the students-as-partners (SaP) model in the higher education sector demonstrates that students, through their lived experiences, have valuable perspectives to contribute to shaping university curricular and co-curricular experiences. While there are numerous inherent benefits associated with facilitating SaP arrangements, incorporating such practices to influence curricular change can be difficult in highly regulated and accredited courses. This article presents a successfully trialled SaP-inspired model involving assessment rubric design in the Bachelor of Laws degree offered at Curtin University in Australia, which is subject to multiple layers of regulation at national and state levels by public and private bodies. The SaP-inspired model presented in the paper is a useful starting point for academics wanting to engage in SaP co-creation of curricular initiatives in contexts that are not especially conducive to SaP, for example, heavily regulated and accredited courses. This article further contributes to existing SaP literature as it presents qualitative and quantitative data collected from the students who engaged in the SaP-inspired model, as well as data collected from students who experienced the SaP-inspired outputs first hand. This article commences with a student reflection on the SaP-inspired model, written by Ryan Kirby who participated in the workshop and assisted in the creation of the assessment rubric and supplementary materials.
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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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".