“It Does Not Do to Dwell on Teaching Notes and Forget to Live”: Instructor Perspectives on Integrating and Adapting Existing Experiential Exercises in Large Classes
Bibliographic record
Abstract
The vast majority of articles describing new experiential exercises are presented from the perspective of the creator(s) of the activity. It is notable, however, that less research has focused on understanding the experiences of instructors who later adopt these exercises in their classrooms. We contribute to the literature on experiential learning in large classes by placing our focus squarely on the user—examining the perceptions, motivations, reflections, and experiences of instructors who integrate existing experiential exercises in large groups. Using a qualitative methodology, we interviewed 12 instructors of a large, multisection undergraduate course at a large Canadian university, in which a common experiential exercise is run across all sections. Two main themes emerged. The first captures the importance of making an exercise one’s own and reflects the variety of strategies that instructors use when preparing to adopt an existing exercise—including active strategies through which the instructors become codesigners of the activity itself. The second reflects different perceptions, ranging from enthusiasm to skepticism, with respect to the value of using experiential exercises. Both themes point to the importance of, as well as strategies designed to enhance, instructor perceptions of self-efficacy in relation to running existing experiential exercises in one’s courses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".