Putting authentic learning on trial: Using trials as a pedagogical model for teaching in the humanities
Bibliographic record
Abstract
Research on authentic learning has been predominantly focussed on skills-based training: there is a paucity of research on models of authentic learning available for adaptation in the humanities undergraduate classroom. In this article, I will seek to address this gap by proposing that legal trials are ideal models for designing authentic learning scenarios in undergraduate teaching and learning contexts, with a specific focus on the humanities. First, I discuss why and how the structure of legal trials can produce authentic learning environments. Second, I present an undergraduate classroom project that combined two disciplinary fields – Shakespearean drama and criminal law – in an effort to enhance student learning and engagement. I outline how the authentic learning scenario (ALS) was implemented and evaluated and, finally, reflect on the barriers, challenges and potentially transformative effect of authentic learning environments on students and educators. This new intervention combines legal studies and English literature in order to create authentic learning environments to increase interactions amongst students, enhance students’ learning, and foster conditions for transformative learning.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".