Unstable Ground: How Mentorship Altered Our View of Experiential and Active Education on Student Learning
Bibliographic record
Abstract
We, as two instructors in Business and Education, sought to explore the research question: is student learning impacted when instructors engage in peer-to-peer mentoring focused on improving understanding of experiential education and active learning in the post-secondary classroom? Within a sociological intrinsic case study framework, we began by defining experiential education, active learning, and peer-to-peer mentoring to situate if instructor interaction in this mentoring model impacts student learning. The data was triangulated for validity between academic literature, thematic coding of instructor/researcher writing, and student surveys. Results revealed that, even though instructors did find some challenges in implementing active learning in their classrooms, there was indication of an overall positive impact on student learning based on the inclusion of these pedagogies as discussed in peer-to-peer mentoring.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".