Moving From “Good” to “Great” SoTL: The Importance of Describing Your Research Epistemological and Ontological Traditions in Your SoTL Scholarship
Bibliographic record
Abstract
This paper explores the metaphor of the “Big Tent” in the context of the scholarship of teaching and learning (SoTL), highlighting the metaphor’s limitations in capturing the complexities and tensions within the scholarly community. This paper delves into the conflicts arising from differing methodologies, epistemological stances, and disciplinary boundaries, viewing them as manifestations of intellectual vigor rather than weaknesses. The paper emphasizes the role of academic training in shaping our perceptions and biases towards educational research and underscores the need for acknowledging these biases in order to foster meaningful dialogue and bridge the diversity in SoTL. We revisit past research on the principles of good practice in SoTL and the shifted focus from “students” to “learners,” acknowledging faculty as perpetual learners in improving teaching practices. The paper proposes an additional principle to elevate SoTL from “good” to “great”: the explicit identification of our SoTL lens. This involves acknowledging our biases, disciplinary perspectives, and methodological preferences in order to enhance the transparency and richness of scholarly conversations. The paper concludes with a call to embrace self-awareness and invites others to do the same, aiming to refine our collective vision and make SoTL endeavors not just inclusive but truly transformative.
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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 | 0.106 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.095 |
| Scholarly communication | 0.037 | 0.039 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.022 |
| 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".