Collaborative Teaching and Creative Assignments Using Contemporary Adaptation
Bibliographic record
Abstract
In this article, we share our perspectives (as teacher and student) on the role of modern adaptations of Chaucer in teaching and assessment, with a particular focus on the role such adaptations play in supporting the use of creative writing-based assignments in a medieval literature course. We describe our experience of an assessment composed of a creative exercise combined with a critical commentary, and discuss how the incorporation of modern adaptations of medieval texts into the medieval literature curriculum underpins and supports this assessment type. Our account demonstrates that the process by which the meaning of literary texts is generated is iterative and collaborative, a point we hope to underscore through our collaboration on this piece. We hope the experience we describe will foreground the value of dialogue in the processes of teaching, assessment, and feedback, and also highlight the role of modern adaptations in supporting students to recognise and articulate the value of their own creative and critical work within a longer tradition of literary and scholarly responses to medieval literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".