From Sherbrooke to Stratford and back again: Team teaching and experiential learning through “Shakesperience”
Bibliographic record
Abstract
Attempting to teach theater in an English Literature course is a daunting prospect. A far cry from the highly individual experience of reading a novel or poem, theater is both a visual and communal kind of engagement. It is a challenge to capture this medium in a traditional lecture-based classroom and harder still to convey its three-dimensionality to undergraduate students. In this paper, we argue that experiential learning and team teaching are especially resonant in the exploration of Shakespearean studies because of the active and collaborative nature of his theater and plays. This paper draws out avenues for experiential learning in the humanities that should have broad applicability and interest a wide range of readers. Framing our design, implementation, and critical reflection in the relevant research, we provide an example of how to anchor experiential learning in the humanities in practice. The case study outlines a compact spring session course on Shakespeare’s plays and performance that includes in-class, online, and field study components. Our research reveals that this approach mirrors in several key ways the collaborative work at the heart of Shakespearean drama and of theater more generally: students are exposed to the plays on the page, on the stage, and behind the scenes; they are offered a model of collaborative knowledge-making both in the theater and in the team-based course design and delivery; and, with these examples before them, they are encouraged to take risks, to collaborate, and to form communities of their own in their learning. In the conclusion we devote attention to funding and the cost associated with experiential learning and field courses. This paper explores experiential learning and field-based immersive learning into the context of disciplinary-specific humanities classrooms with the goal of increasing interaction among students and enhancing students’ learning ( Béchard and Pelletier, 2001 ).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".