Starting from scratch: creating a new learning development provision
Bibliographic record
Abstract
BPP University provides degree and professional qualifications across our Schools of Business, Law, Nursing and Technology, predominantly at postgraduate level, but with a growing undergraduate portfolio. We are also the second largest provider of apprenticeships within the UK and have an international reach, with provision as far afield as Canada and Australia. There are eight locations in the UK, with four in London alone, and we have over 35,000 students globally, 16,000 of whom are international students from over 100 different countries. The institution does not currently have a dedicated learning development team. In early 2025 a group of librarians and learning and teaching specialists came together to create an online academic and study skills resource to support students with their academic skills needs. The project has a strong focus on co-creation with students. We aim to create resources to enhance the student academic skills experience, with a focus on accessibility and ensuring that students’ needs are met through collaboration with them. In this practitioner mini keynote, we outlined the project approach and outcomes so far and invited our delegates to discuss the following prompts: How have you enhanced the accessibility of resources at your institution? Do you co-create with students and work with them to understand their needs? Have you sought to understand the impact and value of the academic skills resources within your institution? And if so, how?'
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".