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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".