Elementary school teachers’ perspectives about learning during the COVID-19 pandemic
Bibliographic record
Abstract
How did school closures affect student access to education and learning rates during the COVID-19 pandemic? How did teachers adapt to the new instructional contexts? To answer these questions, we distributed an online survey to Elementary School teachers (N = 911) in the United States and Canada at the end of the 2020-2021 school year. Around 85.8% of participants engaged in remote instruction, and nearly half had no previous experience teaching online. Overall, this transition was challenging for most teachers and more than 50% considered they were not as effective in the classroom during remote instruction and reported not being able to deliver all the curriculum expected for their grade. Despite the widespread access to digital technologies in our sample, nearly 65% of teachers observed a drop in class attendance. More than 50% of participants observed a decline in students' academic performance, a growth in the gaps between low and high-performing students, and predicted long-term adverse effects. We also observed consistent effects of SES in teachers' reports. The proportion of teachers reporting a drop in performance increases from 40% in classrooms with high-income students, to more than 70% in classrooms with low-income students. Students in lower-income households were almost twice less likely to have teachers with previous experience teaching online and almost twice less likely to receive support from adults with homeschooling. Overall, our data suggest the effects of the pandemic were not equally distributed.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".