Reconsidering the Mandatory in Ontario Online Learning Policies
Bibliographic record
Abstract
In March 2019, the Ontario government announced that commencing in 2023-24, secondary school students (Grades 9-12) would be required to gain four of 30 graduation credits through online courses. At the time of the policy pronouncement, these four credits (or courses) would become the first mandatory online courses in Canadian K-12 education. The policy decision and process were challenged publicly, and the educational context changed quickly with the ensuing contingencies of the global pandemic. The policy was subsequently revised and, at present, Ontario requires two mandatory online secondary school credits for graduation, which is twice the requirement of any other North American jurisdiction. In this study, the researchers employ a critical policy analysis framework to examine the concept of mandatory online learning in Ontario through multiple temporal contexts. First, they examine Ontario’s mandatory online learning policy prior to the shutdown of Ontario schools during the 2020-2021 global pandemic. Next, they examine aspects of Ontario’s mandatory online learning policy in K-12 during the emergency remote learning phase of the pandemic. In the final section, the authors provide a retrospective analysis of the decisions around mandatory e-learning policy and explore policy options going forward for mandatory e-learning in the K-12 sector post-pandemic.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".