Leadership for the Scholarship of Teaching and Learning: Understanding Bridges and Gaps in Practice
Bibliographic record
Abstract
The gap between the practice of individual academics based on the ideal of the SoTL—improving student learning—and the institutional infrastructure and leadership to support that work is an ongoing challenge to the development of the field (Hutchings, Huber, & Ciccone, 2011; Poole, Taylor, & Thompson, 2007; Simmons, forthcoming). To better understand how individuals in diverse roles contribute to the development of the SoTL in the context of their institutional cultures, this study examined how faculty, educational developers (EDs), and administrators enact SoTL leadership. A grounded theory approach (Leedy & Ormrod, 2001) guided the development of a survey that used closed and open-ended questions to invite respondents to share their personal conceptions and lived experiences of the SoTL. Drawing on the responses received (n=75), we identified ways faculty, educational developers, and administrators construe their SoTL leadership roles and how they can fulfill a vital role in facilitating leadership across and beyond their institutions to create critical social networks for SoTL work (Mårtensson, Roxå, & Olsson, 2012; Williams et al., 2013) and contribute to institutional cultures that support and value that work. The results reveal how gaps between the work of individual scholars and the cultures of their academic communities are being bridged through diverse leadership roles that cross multiple levels in their institutions and identify some of the gaps that remain.
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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 | 0.068 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.040 | 0.085 |
| Scholarly communication | 0.039 | 0.028 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".