Are We Ethically Bound to Use Student Engagement Technologies for Teaching Law
Bibliographic record
Abstract
\n\t\t\t\t\tWhat conscientious law professor of first year, large format classes in torts, contracts, or criminal law has not pondered how to better engage students while easing their reluctance to speak out in class? While students entering law schools are quite adept with student engagement technologies (SETs) from undergraduate classes, some law faculties seem tied to the passive environment of lectures and PowerPoint presentations and hence reject SET methodologies as so much techno-wizardry. With the entry of webbased programmes into the expanding field of SETs, and increasing empirical evidence that active learning improves grades and closes gender and socio-economic gaps, the ethical question arises, are we not obliged as law teachers to employ them? This paper examines in three steps that gap between pronouncing from the podium and actively engaging learners by clicker response or web-based devices. Part I reviews the growing literature on active learning including SET-based methods. Part II examines two models of SETs, remote-based and web-based, for their comparative attributes and drawbacks, with a particular focus on law teaching. Part III details the author’s experiences with the clicker system teaching introductory law and criminology and offers practical suggestions for facilitating its use. The paper concludes that, in light of recent evidence of heightened learning success using active learning methodologies, and the impending complexity to education posed by wearable technologies, the ethical question of pedagogical competence grows in importance.\n\t\t\t\t
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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 | 0.009 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".