SoTL Clusters: Faculty-Focused Needs-Based Scholarship of Teaching and Learning Support
Bibliographic record
Abstract
To further teaching and learning, there has been a push to engage faculty to pursue research on teaching. With the recognition that science research often is done by teams, we sought to create a research-cluster approach to support faculty engaging in the Scholarship of Teaching and Learning (SoTL). This article describes a faculty-focused process for co-designing and implementing a SoTL Clusters approach to supporting faculty. Faculty shifting from disciplinary research to engaging in SoTL research identified several needs, including recognition for SoTL work, mentorship in SoTL work, reduced isolation of investigators, funding, and promotion. By engaging faculty in a needs-based design process to identify relevant components for the program, the resulting SoTL Clusters model had a strong uptake by STEM faculty. Evaluation findings 2 years after the launch at a comprehensive university indicated that the program addressed most needs, including peer networking, time for SoTL, skills development, and publishing requirements; additional considerations for communication and publishing were suggested. This process of applying needs-based design and the SoTL Clusters model offers a promising informed approach to co-creating SoTL supports that address needs and are responsive to the disciplinary and institutional context, particularly for STEM educators.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".