Educational Leadership in Teaching Excellence (EnLITE): A Peer-Driven Faculty Development Program
Bibliographic record
Abstract
Educational Leadership in Teaching Excellence (EnLITE) is an 11-month faculty development program at the University of Guelph, Ontario. Created and led by faculty members and educational developers, EnLITE is designed to engage participants in the principles, practice and theory of teaching and learning in higher education and to promote a learner-centred approach to teaching. Participants critically examine and discuss scholarly topics on teaching and learning and in their own disciplines; collaborate with one or more teaching mentors; engage in peer classroom observation; and participate in other teaching-related activities informed by their individual learning plans. Our objective was to determine the perceived impact of EnLITE on participants’ teaching-related practices and experiences. We collected pre-, post- and one-year post-program quantitative and qualitative survey responses from each of the 2014-2015, 2015-2016 and 2016-2017 EnLITE cohorts (N = 17 participants representing a variety of disciplines; 71% female). There were significant improvements in participants’ perceived teaching practices related to critical self-reflection (13% increase from pre- to one-year post-program), student engagement (+28.2%), collaborative learning (+31%) and learner-centred pedagogy (+22.9%, all p < 0.05). There was little to no change in use of technology, student assessment, leadership, participation in communities of practice, or dissemination of teaching-related scholarship. These results provide empirical evidence of the effectiveness of a peer-driven faculty development program in promoting a learning-centred approach to teaching. Future research should determine whether these changes translate into improved student learning, and whether such programs demonstrate longer term improvements in engagement in teaching-related leadership, communities of practice and dissemination.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".