Supporting student writing and other modes of learning and assessment: a staff guide
Bibliographic record
Abstract
Academic writing in Higher Education (HE) is contested practice freighted with meaning, never more so than for widening participation students, still placed as ‘outsiders’ and often left feeling unwelcome and ‘un-voiced’. Ironically, as Molinari (2022) argues, universities were originally more diverse in form and content, not heavily ‘literate’ but oral, discursive and creative. As HE has become ostensibly more ‘open’ the system has become more normative, more formally rule-bound, more ‘written’ – and hence more exclusive. A recent example in the UK is the Office for Students’ attack on inclusive assessment, pushing instead for more emphasis on spelling, punctuation and grammar. Alongside this tension, many in the Learning Development (LD) community feel that discipline academics do not see the ‘teaching’ of academic writing as part of their pedagogic and assessment repertoire, preferring to send students to LD ‘to be fixed’. However, academics and LDs engaged in discussion and free writing (Elbow 1998, 1999) on this topic at a LondonMet L&T Conference presented views that were more nuanced and sympathetic. There was a deep appreciation of the ‘real’ work that academic writing does with and for students; but also a sense that they did not know how to build writing into their practice(s). And so was born this staff Guide: a playful, creative and yet intensely practical guide for academic staff who want to empower their students to write – often, playfully, experimentally – on their way to ‘becoming’, and becoming academic. Presenting the Guide in the resource showcase allowed us to highlight the continuing centrality of writing. Lecturers and university staff can use it to engage students in ‘writing to learn’ rather than ‘learning to write’.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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".