The Power of Social Networks: A Model for Weaving the Scholarship of Teaching and Learning into Institutional Culture
Bibliographic record
Abstract
This paper offers a guide for those seeking to integrate the Scholarship of Teaching and Learning (SoTL) into higher education institutions to improve the quality of student learning. The authors posit that weaving SoTL into institutional cultures requires the coordinated actions of individuals working in linked social networks rather than individuals acting in isolation. Analyzing both the barriers and potential pathways to integrating SoTL into institutional cultures, the authors provide a conceptual model along with examples of practical strategies for overcoming resistance to change within institutions. The paper provides examples from a variety of different international contexts to show how incentives and other non-coercive measures can motivate faculty and administrators to weave SoTL into institutional fabrics. Drawing on social network theory and the concept of communities of practice, the paper presents a model with attendant strategies for disseminating SoTL values and practices across all three levels of postsecondary institutions: the micro, the meso, and the macro. The authors argue that for SoTL to take root in organizational cultures, there must be 1) effective communication and dissemination of SoTL activity across all levels, 2) well established social networks and links between these levels (nodes), and 3) sustained support by senior administration. The authors conclude by suggesting ways their model could be tested.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".