Using Infographics to Go Public with SoTL
Bibliographic record
Abstract
There has been a call to amplify the scholarship of teaching and learning (SoTL) and expand its reach by engaging with audiences outside the academy. In this paper, we share our journey in crossing disciplinary boundaries and creating a SoTL-informed infographic for public consumption. As the field of SoTL continues to evolve, infographics hold tremendous potential to communicate SoTL to various stakeholders, including educators, students, administrators, policymakers, and the public. We outline best practices in infographic development and the potential of infographics as a tool for taking SoTL public, emphasizing their visual appeal and effectiveness in conveying complex information. We conclude by discussing the implications of using infographics to advance SoTL communication. The efforts of our group serve as a valuable example of how infographics can be used to bring SoTL knowledge out of academia and into the public domain.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".