A Systems-Inspired Taxonomy of SoTL Research: Increasing the Accessibility and Visibility of this Heterogeneous Field
Bibliographic record
Abstract
The scholarship of teaching and learning (SoTL) is a vast, multi-epistemic field which can be challenging for new and experienced scholars to navigate. This paper introduces a systems-inspired taxonomy of SoTL designed to enhance accessibility, visibility, and knowledge organization within the field. Drawing from an iterative and extensive process involving a literature review, thematic coding of published SoTL inquiries from eight different journals, and international community consultations, the resulting taxonomy in its current form has six trees or “dimensions.” These include who is being studied, what aspects of learning are investigated, how learning is supported, where and when studies take place, why students learn, and the inquiry approaches used. The taxonomy serves multiple purposes: providing researchers with a structured way to situate their work, guiding literature and scoping reviews, and improving the discoverability of SoTL studies through more deliberate keyword selection. By adopting a systems-thinking perspective, this taxonomy balances structure with flexibility, offering a navigational tool rather than a rigid classification scheme. It is intended to be descriptive, rather than prescriptive, and should be regularly updated through ongoing community input and engagement in order to ensure it remains reflective of emerging research and practice in the field.
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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 | 0.095 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".