Educational Leadership in Teaching Excellence: The University of Guelph EnLITE Program
Bibliographic record
Abstract
Educational Leadership in TeachingExcellence (EnLITE) is a one-year program (Sept to Aug) at the University ofGuelph. It is designed to engage mid-careerfaculty in the theory, practice and scholarship of teaching and learning, andto establish and support a faculty community of practice which providesmentorship and leadership in teaching and learning in higher education. Divided into two subprograms, faculty participantsenrolled in EnLITE I critically examine and discuss scholarly topics onteaching and learning and in their own disciplines; collaborate with a teachingmentor; engage in classroom observation and peer feedback; and demonstratecommitment to continual improvement through completion of an individual programlearning plan, critically reflective teaching practice, and creation of anelectronic teaching dossier (ePortfolio). Participants meet twice monthly, in the larger cohort and in smaller groupscalled “Action Learning Sets.” Those wishing to engage in pedagogicalresearch may concurrently or subsequently enrol in EnLITE II, also a one-yearprogram. Participants in EnLITE II develop, implement and disseminate researchon teaching and learning in higher education, and are expected to demonstratehow results of their research inform their teaching practice. Participants meetmonthly in Action Learning Sets. Eachparticipant in EnLITE I and II embarks upon a process unique to theirindividual goals and objectives. The expectedtime commitment for each program is approximately 5 hours per week. Participants’progress is evaluated on a pass/fail basis against their own individual learningplan, and program outcomes. We see commitment to teaching and learning as beingrewarded both in the classroom from students, as well as faculty satisfaction andincreasingly, in tenure and promotion decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".