COVID‐19 school closures and educational achievement gaps in Canada: Lessons from Ontario summer learning research
Bibliographic record
Abstract
The 2020 COVID-19 pandemic closed most Canadian public schools for six consecutive months between March and September. This paper explores possible impacts of that closure on student achievement. Longstanding research suggests that lengthy periods of time out of school generally create losses of literacy and numeracy skills and widen student achievement gaps. New American studies have attributed sizeable learning losses to the COVID-19 closures. In lieu of comparable Canadian data, this paper extrapolates from summer learning research to estimate likely shortfalls in literacy and numeracy skills. We draw on data from 14 cohorts of Ontario primary-grade students collected between 2010 and 2015 in which 3,723 attended summer programs and 12,290 served as controls. Across three plausible scenarios, we use meta analyses and OLS and quintile regression models to predict learning losses of 3.5 and 6.5 months among typically-performing and lower-performing students respectively, and achievement gaps that grow up to 1.5 years among same grade peers. After qualifying these predictions, we recommend that provincial ministries offer targeted supplementary programs during the summer and synchronous instruction in the event of future school closures.
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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".