Professional development and recognition opportunities for learning development practitioners: international perspectives
Bibliographic record
Abstract
Learning Development (LD) practitioners have access to an expanding range of professional recognition and development opportunities (see Briggs, 2023). However, reports from members of the LD community highlight variations in the extent to which CPD engagement is facilitated and supported. Associated research that has sought to objectively establish trends pertaining to the factors that inhibit, or support engagement is limited. This 2024-25 ALDinHE funded international research study addressed this gap in knowledge through establishing the factors that impact on LD practitioner access to and engagement with professional development and recognition. To facilitate meaningful comparisons of LD practitioners a taxonomy of LD roles was also developed (as proposed by Briggs, 2025). In autumn 2024, an online questionnaire (comprising open and closed questions) was sent to Academic Language and Learning Development Practitioners. This was administered with support of the International Consortium of Academic Language and Learning Developers (ICALLD) membership and included UK (ALDinHE), Australia (AALL), New Zealand (ATLAANZ), Canada (LSAC) and South Africa. Responses were analysed through a mix of established qualitative and quantitative methods. In this session we shared our proposed thematic taxonomy of LD roles. We then presented results detailing the personal, institutional, national or international factors found to support or inhibit the professional development, recognition and promotion routes available to Academic Language and LD Practitioners. We invited attendees to discuss and share reflections on our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".