Developing criteria to assess graduate attributes in students' work for their disciplines
Bibliographic record
Abstract
After two decades, efforts to integrate the development and assessment of ‘graduate attributes’ into discipline curricula remain slow, uneven, and fraught with difficulties. Scholars have identified political, cultural and practical reasons for academics’ resistance to this requirement, including ‘lack of ownership and shared understanding of how to teach and assess graduate attributes’ (Radloff et al., 2008). Along with Barrie (2007) and de la Harpe and David (2010), Radloff et al. (2008) have argued that ‘academic staff beliefs are critical and fundamental to any attempts at developing students’ graduate attributes’.This article suggests that, rather than trying to change these beliefs via top-down mandates to adopt institutional attributes, it may make sense instead to start from academics’ beliefs and see what attributes they suggest are actually integral to their cultures of enquiry. I reflect on such a process in the context of developing criteria and standards for assessing graduate ‘capabilities’ across the three years of a BA degree, in which a Faculty working party tried to tease out what we meant by ‘good writing’ into easily applicable criteria with authentic meaning(s) across our varied disciplines.
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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 | 0.054 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".