Community building for pedagogic and professional impact: developing a business school teaching and learning forum
Bibliographic record
Abstract
Background & Context Communities of practice (CoPs) are ‘groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly’ (Wenger-Trayner and Wenger-Trayner, 2015). The importance of CoPs within higher education is well established (McDonald and Cater-Steel, 2016). Successful CoPs can, for example, support learning and development (Nistor et al., 2014), facilitate knowledge exchange and resource sharing (Nagy and Burch, 2009), and promote collegiality (Ryan, 2015). CoPs may be particularly valuable for education-focused faculty who, despite their increasing representation within the academic workforce, continue to face challenges in relation to the expectations placed on them, lack of clarity regarding requirements for career advancement, and attaining parity of esteem with research-focused peers (Smith and Walker, 2024). In 2019/2020, a CoP in the form of a Teaching and Learning (T&L) Forum was developed by Dr Danielle McConville at Queen’s Business School (QBS), Belfast. The stated intentions were to create a space for discussions about teaching and learning and to build belonging and recognition for education-focused faculty. The Forum is organised by a voluntary committee consisting of staff from across QBS. Practice & Impact Over the last five years, the QBS T&L Forum has organised a wide range of formal seminars and workshops, as well as an annual ‘Festival of Teaching and Learning’. These address, amongst other topics, pedagogic theory, teaching practice, and advances in educational technology. They are supplemented by informal ‘coffee and conversation’ sessions without a set agenda. In 2021, a ‘Scholarship of Teaching and Learning (SoTL) Seed Funding Competition’ was introduced. The Forum has been very well received by stakeholders, including accrediting bodies. Faculty have reported that it has helped to enhance the quality of and increase respect for teaching. To quote two participants: ‘It is a rare opportunity to discuss learning and teaching issues, which are often neglected or left in silence’ and ‘Great forum to share ideas and a fantastic way to connect with colleagues’. Recent innovations include regular writing retreats and there are plans to develop more online resources. Challenges faced include encouraging engagement in busy semesters especially beyond an engaged core of regular attendees.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".