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 ââ¬Ëgraduate attributesââ¬â¢ into discipline curricula remain slow, uneven, and fraught with difficulties.àScholars have identified political, cultural and practical reasons for academicsââ¬â¢ resistance to this requirement, including ââ¬Ëlack of ownership and shared understanding of how to teach and assess graduate attributesââ¬â¢ (Radloff et al., 2008). Along with Barrie (2007) and de la Harpe and David (2010), Radloff et al. (2008) have argued that ââ¬Ëacademic staff beliefs are critical and fundamental to any attempts at developing studentsââ¬â¢ graduate attributesââ¬â¢.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 academicsââ¬â¢ 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 ââ¬Ëcapabilitiesââ¬â¢ across the three years of a BA degree, in which a Faculty working party tried to tease out what we meant by ââ¬Ëgood writingââ¬â¢ into easily applicable criteria with authentic meaning(s) across our varied disciplines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".