Co-Teaching an Online Action Research Class / Co-enseignement et classe de recherche-action en ligne
Bibliographic record
Abstract
Two instructors report our experience co-teaching an action research (AR) required as part of an e-learning master’s degree. Adopting a practice-centered stance we focus on the course activities of participants (instructors and students), with particular attention to the careful crafting of course elements with the goal of achieving an excellent learning experience for students. The case narrative describes the course and ways in which we have modified the course based on a variety of considerations. We also outline problems and areas still in need of improvement. We reflect on the role of theory in our own pursuit of excellence, and the role of theory in our students’ inquiry processes. We find that theory is just another tool or resource to apply to the work, with the core concerns being the needs of students and the learning environment. Deux enseignants font le rapport de leur expérience de co-enseignement d’un projet de recherche-action requis pour un cours de formation en ligne au niveau de la maîtrise. À l’aide d’une approche axée sur la pratique, nous nous sommes concentrés sur les activités de cours des participants (enseignants et étudiants), avec une attention particulière pour l’élaboration minutieuse d’éléments de cours. Il s’agissait finalement de créer une expérience d’apprentissage enrichissante pour les étudiants. L’exposé décrit le cours et les façons par lesquelles nous avons modifié le cours à partir de considérations diverses. Nous donnons également un aperçu des problèmes et secteurs nécessitant des améliorations. Nous nous sommes penchés sur le rôle de la théorie dans notre propre quête d’excellence et dans le processus d’enquête de nos étudiants. Nous concluons que la théorie n’est qu’un outil ou une ressource s’appliquant au travail et qu’il faut davantage se préoccuper des besoins des étudiants et de l’environnement d’apprentissage.
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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 | 0.010 | 0.025 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".