Instructor Reflections from a Graduate-level Course on STEM Education Research and Practice
Bibliographic record
Abstract
The University of Guelph has piloted a graduate-level course on STEM Education. The course sought to balance research and practice lenses, including learning outcomes and activities related to learning theories, epistemologies, research methodologies, and an embedded Instructional Skills Workshop. The course was intentionally designed to target affective learning domains, challenging learner beliefs and values about teaching and research. The aim is to share critical reflections from the co-instructors following the first offering of the course, with a focus on philosophical approaches, expectations, student perceptions, threshold concepts, and the negotiation of meaning. Key themes identified in both reflections included: exploration, transformation, climate & community and logistics & operations. These themes illustrate learning as a process, depending on the openness of learners to question pre-existing beliefs, and re-build existing mental models. The resulting transformation is aided by collaboratively learning with others, where different perspectives and values help students uncover their own.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".