Hybrid Courses and Online Policy Dialogues: A Transborder Distance Learning Collaboration
Bibliographic record
Abstract
This essay describes a blended (hybrid) course collaboration used to facilitate policy dialogues between graduate students at two institutions (one in Canada and the other in the US) as a way to teach about policy. The course content and design is informed by three trends in research and practice: increased policy borrowing across boundaries and jurisdictions; calls to democratize policy making in general and in education policy in particular; and developments in teaching and learning online. Drawing on students’ informal feedback in combination with reflections on instructors’ experiences, we suggest that policy dialogues are a promising strategy for promoting students’ learning about education policy. We also illustrate how professors can use a hybrid course structure between two institutions. Cet essai décrit la démarche de collaboration lors d’un cours hybride visant à faciliter les dialogues politiques entre les étudiants de troisième cycle de deux établissements (un au Canada et l’autre aux États-Unis). Cette collaboration est une façon de former les étudiants au sujet de la politique. Le contenu et la forme du cours reposent sur trois tendances en matière de recherche et de pratique : accroissement de l’emprunt des politiques au-delà des frontières et des juridictions; appels à la démocratisation de l’élaboration de politiques en général et de celles relatives à l’éducation en particulier; et évolution de l’enseignement et de l’apprentissage en ligne. En nous basant sur les commentaires informels des étudiants et sur les réflexions des enseignants à propos de leurs expériences, nous suggérons que les dialogues politiques constituent une stratégie prometteuse pour promouvoir l’apprentissage des étudiants en matière de politiques sur l’éducation. Nous illustrons aussi la façon dont les enseignants peuvent utiliser une structure de cours hybride entre deux établissements d’enseignement.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".