From Full Day Learning to 30 Minutes a Day: A Descriptive Study of Early Learning During the First COVID-19 Pandemic School Shutdown in Ontario
Bibliographic record
Abstract
When the COVID-19 pandemic was declared in March 2020, the lives of families all over the world were disrupted. Many adults found themselves working from home while their children were unable to go to school. To better understand the potential impact of these educational disruptions, it is important to establish what learning looked like during the first school shutdown in the spring of 2020, particularly for the youngest learners who may feel the longest lasting impacts from this pandemic. Therefore, the purpose of the current descriptive study was to gather information on how kindergarten teaching and learning occurred during this time, what the biggest barriers were, and what concerns educators had regarding returning in person to the classroom setting. The sample for the current study was 2569 kindergarten educators (97.6% female; 74.2% teachers, 25.8% early childhood educators) in Ontario, Canada. Participants completed a questionnaire consisting of both quantitative scales and qualitative open-ended questions. Educators reported that parents most often contacted them regarding technological issues or how to effectively support their child. The largest barrier to learning was the ability of both parents and educators to balance work, home life, and online learning/teaching. With regards to returning to school, educators were most concerned about the lack of ability of kindergarten aged children to do tasks independently and to follow safety protocols. Our findings highlight unique challenges associated with teaching kindergarten during the pandemic, contributing to our understanding of the learning that occurred in Ontario during the first COVID-19 shutdown. Supplementary Information: The online version contains supplementary material available at 10.1007/s10643-021-01304-z.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".