How Experienced SoTL Researchers Develop the Credibility of Their Work
Bibliographic record
Abstract
Teaching and learning research in higher education, often referred to as the Scholarship of Teaching and Learning (SoTL), is still relatively novel in many academic contexts compared to the mainstay of disciplinary research. One indication of this is the challenges those who engage in SoTL report in terms of how this work is valued or considered credible amongst disciplinary colleagues and in the face of institutional policies and practices. This paper moves beyond the literature that describes these specific challenges to investigate how 23 experienced SoTL researchers from five different countries understood the notion of credibility in relationship to their SoTL research and how they went about developing credibility for their work. Semi-structured interviews were facilitated and analyzed using inductive analysis. Findings indicate that notions of credibility encompassed putting SoTL research into action and building capacity and community around research findings, as well as gaining external validation through traditional indicators such as publishing. SoTL researchers reported a variety of strategies and approaches they were using, both formal and informal, to develop credibility for their work. The direct focus of this paper on credibility of SoTL work as perceived by experienced SoTL researchers, and how they go about developing credibility, is a distinct contribution to the discussions about the valuing of SoTL work.
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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 | 0.091 | 0.276 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".