Pedagogical Design and Development of Training Systems in the Context of a Pandemic
Bibliographic record
Abstract
The insidious spread of the COVID-19 pandemic has led to containment measures in Canada and many other countries, requiring teachers of all levels to engage in distance education. As a result, the Quebec Ministry of Education tasked Université TÉLUQ with creating a distance education program to train the province's teachers to teach remotely. The goal was to determine how to quickly prepare educators from different levels and grades to move from in-person to remote instruction. For this workshop, I will therefore discuss the process involved in creating the J'enseigne à distance [I teach remotely] program and free asynchronous online course that requires no registration but includes an accreditation option. J'enseigne à distance comprises four microprograms: "support," "disseminate," "adapt" and "assess" as well as support (for higher education) including webinars, a toolbox and a glossary specifically designed to help teachers adapt to the remote teaching environment. I will highlight the changes the project is currently undergoing, the collaborations underway to adapt the training to the specific educational levels, and the difficulties that had to be overcome in an emergency and telework context. Lastly, I will share attendance data as well as the perceived benefits and limitations of this kind of training.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".