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Record W4200523229 · doi:10.18357/otessaj.2021.1.2.12

Reconsidering the Mandatory in Ontario Online Learning Policies

2021· article· en· W4200523229 on OpenAlexaffvenueabout
Lorayne Robertson, Bill Muirhead, Heather Leatham

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGraduation (instrument)Context (archaeology)JurisdictionGovernment (linguistics)Political sciencePandemicPublic relationsBusinessCoronavirus disease 2019 (COVID-19)EngineeringMedicineLawGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.268
GPT teacher head0.461
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes3
Has abstractyes

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