Writing to learn: creative LD perspectives for Learning Developers and students
Bibliographic record
Abstract
Academic writing is a contested area, even more so in times of large language models and artificial intelligence (AI). This writing is tricky to navigate and master especially for newcomers – staff and students. Learning Developers almost uniquely play with writing as a practice of emergence and discovery. Academic writing is a process: we write to become academic. Students write to join their epistemic communities, and Learning Developers write to give birth to an emergent field. Drawing on recent work by Syska and Buckley (2022) and Abegglen, Burns and Sinfield (2022; 2023), we argue that academic writing is an initiation into and participation in wider professional and academic discourses. We ‘write to learn’ rather than ‘learn to write’. In our practice with students, we know that we need to move beyond the ‘mechanics’ of writing and make the process meaningful, engaging, interactive, and fun. Similarly, Syska and Buckley (2022) have explored what makes Learning Developers ‘tick’ with respect to academic writing – revealing how, counterintuitively perhaps, academic writing can become an inclusive Learning Development space: our ‘happy place’. With this presentation, we opened the discussion on academic writing for building the Learning Development community.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.035 | 0.089 |
| Scholarly communication | 0.050 | 0.034 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".