Empowering Educators through SoTL: Insights and Innovations from Real-Time Audience Engagement
Bibliographic record
Abstract
This is an invited article based on a HERDSA keynote address, which was presented at the 2024 conference held in Adelaide, 8-11 July. The data set underpinning this article has been published separately in this issue of the ASRHE journal (Schönwetter, 2025; https://doi.org/10.59197/asrhe.v6i1.13515). Abstract The Scholarship of Teaching and Learning (SoTL) is a conceptualisation of the teaching role in higher education that can transform academic understanding and practices by incorporating research as integral to teaching - an evidence-based and theoretically informed approach to teaching. Moreover, SoTL involves the systematic study of teaching and learning processes with a singular goal: improving educational practices and, most importantly, enhancing student outcomes. It is a form of research that holds transformative power, not only in how we teach but in how we elevate student learning to its fullest potential. This study explored the perceptions of academics related to SoTL and what supports would empower uptake and advocacy of SoTL. This was accomplished by engaging HERDSA keynote attendees through real-time audience polling. Guided by a participatory action research (PAR) approach, data were gathered in an online format (Mentimeter) from 263 participants during the keynote, responding to 10 questions related to SoTL, institutional challenges, and future action. This study highlights key areas where educators seek clarity and offers insights on how HERDSA can support its membership by addressing SoTL barriers, enhancing collaborative networks, and fostering continuous professional development. The implications of these findings extend to HERDSA’s potential role in shaping the future of SoTL practices and empowering its members to advocate for SoTL within their institutions.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".