The Equity of Skills: A Case Study of Ontario’s K-12 Learning Priorities During the COVID-19 Pandemic
Bibliographic record
Abstract
Since March 2020, over 5 million students across Canada’s publicly funded schools were impacted by the COVID-19 pandemic with elementary and secondary schools in Ontario closed for at least 27 weeks. Now in the wake of the pandemic, concerns have been raised about the changes imposed from the COVID-19 school closures in Ontario and how that has impacted student skill development, especially for the most marginalized learners. Through an instrumental case study approach, this study provides a behind-the-scenes look from 15 education scholars and researchers, education leaders, and policy professionals on Ontario’s learning priorities and educational inequities prior to and during the pandemic. This study applies a qualitative approach and employs critical theory in education to provide an opportunity to interrogate the role of power and inequity in Ontario’s education system and the political landscape during the pandemic. Findings reveal the skills being emphasized prior to the pandemic shifted in the transition to online and remote learning. Participants discussed that prior to the pandemic there was stronger emphasis on 21st century skills, well-being and mental health; while during the pandemic, 21st century skills were needed for students to cope with learning from home. During the pandemic, participants identified the emphasis on basic skills (literacy, numeracy, and writing) was part of the learning loss discussion, reinforcing how expectations for students remained consistent despite global disruption. Findings also reveal that learning opportunities were not equal for all learners during the pandemic, where students from the most marginalized backgrounds namely students with disabilities, as well as Black, Indigenous, and students from low-income families experienced the most challenges ranging from accessing Internet to achievement gaps. This study contributes to the limited knowledge base that connects skills development and educational inequities in Ontario’s education system, and it provides a unique view into the experiences of education scholars, leaders, and policymakers and policy advisors during a once-in-a-generation global pandemic. Based on the insights from this study, recommendations are suggested for how Ontario’s education system can build back better and reimagine learning post-pandemic to better serve all students.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".