Raising the profile of Learning Development: thinking forwards
Bibliographic record
Abstract
Following an article comparing ‘study skills’ provision to J.M. Barrie’s Tinkerbell (Richards and Pilcher, 2023), discussions – and emotions – were stirred again this year regarding how Learning Development is understood among academic and third-space colleagues. Inspired by White and Webster’s (2023) session at last year’s ALDcon, the Study Advice service at the University of Reading, in collaboration with Dr Helen Webster from the University of Oxford, decided to run an ALDinHE regional event to address this very question: how do Learning Developers promote a better understanding of what we do, and raise our profile in our institutions? This presentation reported back from this regional event, sharing both the barriers and proposed solutions to raising our profile. But there is still work to do. Together we hope to create a useful action plan from this work. We discussed whom we need to communicate with, what we feel these messages should be, and how we claim our expertise. Finally, we considered what we can do as a cross-institutional collective to ensure that we are seen as a profession with our own expertise and identity.
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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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.029 | 0.067 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".