‘When Everything Old Is New Again’: Experiential Learning in the Classroom
Bibliographic record
Abstract
This session examined definitions of experiential learning and how the term has been recently used to focus on specific programmes and types of learning practices at the expense of other programmes, often based in the Humanities, as well as more routine pedagogical strategies. Further, the current use of the terminology creates a false and damaging dichotomy as it situates the classroom (and even the wider locale of the campus) away from the “real world” and encourages students to compartmentalize classroom learning as incidental to their lives and future careers. We encouraged participants to examine their current classroom practices to identify activities that may be considered experiential and asked them to identify obstacles they face both in their disciplines and generally that prevent them from incorporating more of these practices in their classrooms. We tried to model how small changes in teaching practices can encourage students to engage in their own learning. We contend that one does not have to reinvent the wheel—or rewrite the curriculum—to offer students experiential educational opportunities. Many of the activities instructors already use can be adapted to encourage students to participate more fully in their own learning and, with simple changes, these practices can be used in any classroom. Our hope was that participants would recognize how they might engage their students in experiential learning without leaving their classrooms.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".