Educational Development Leadership: A Distributed Leadership Case Study of a University Teaching and Learning Centre
Bibliographic record
Abstract
Teaching and learning centres at universities lead educational development efforts to improve and innovate teaching at universities, yet these centres face many challenges. It has been argued that they could adopt a strategic organizational leadership approach to meet these challenges, but how this can be theoretically and practically achieved is an open question. This case study explored the use of social network analysis (SNA) and distributed leadership as theoretical and practical tools. The primary questions guiding the initial inquiry were (1) How can leadership in teaching and learning in a large research intensive university be understood as a distributed network? (2) How do SNA concepts (such as centrality, betweenness and density) help to illuminate distributed influence within this network and how do they relate to participants’ demographic characteristics (such as discipline, formal role, professorial status, number of years teaching, department size, professional development as an educator, and number of years at the university)? The research began with a comprehensive literature survey on: teaching and learning centres, educational development, and higher education leadership for teaching and learning. The resulting conceptual framework incorporated distributed leadership and social network analysis. A second literature review focused solely on distributed leadership, critically reviewing the extant publications in higher education contexts. Normative perspectives were found to be dominant, while analytical perspectives were lacking. Empirically, the dissertation follows from these results to map out a leadership network based on advice and information seeking behavior. Through purposive sampling of formal educational development leaders, and snowball sampling of formal and informal leaders identified through advice and information-seeking behavior and recommendation, an institutional network map educational development leadership emerged. 196 interviews were conducted at a large, research-intensive university in Canada. Informal leaders included not only full-time professors, but also part-time professors, support staff, and teaching assistants. Formal leaders who were identified and interviewed included deans and vice deans, directors, chairs, and a vice-provost. Correlations between centrality measures (in-degree, betweenness, closeness, or eigenvector) and demographic variables (professional development as an educator, years at organization, years as an educator, organizational level, formal leadership position, has conducted educational research were investigated using Pearson’s r, but no significant relationships were identified. In addition to analysis of the network, a researcher reflective journal supports the case study.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".