Superintendents' Experiences with Distance Learning Practices in K–12 Public-School Districts in New York During the COVID-19 Pandemic
Bibliographic record
Abstract
In early March 2020, the United States was first faced with the COVID‑19 virus, which became a pandemic affecting 216 countries across the globe (Worldometers.info, 2020). This pandemic impacted approximately 1.5 billion learners globally by schools’ closures and revised practices (United Nations Educational, Scientific, and Cultural Organization [UNESCO], 2020), and it required social distancing practices and school closures across the U.S. (Lieberman, 2020). Due to closures, public-school districts were tasked with creating immediate solutions for seamless learning. Many public-school districts implemented distance learning practices to meet the needs of quarantined students. The sudden shift from a traditional, in-person classroom to a distance learning setting challenged both faculty and students. This qualitative case study examines how superintendents in K–12 public schools shifted from on-site learning to distance learning practices during the pandemic. Thirty superintendents from K–12 public-school districts in two suburban counties of New York participated in an online survey. By mid-March 2020, according to the findings, distance learning was implemented in varying degrees. In addition, the study found that most faculty were prepared for online learning through professional development. Parental support, technology, and the inability to work independently were barriers to student learning. The findings suggest ramifications from the delivery of distance learning in the first months of the pandemic. In addition, the study found a need for increased professional development and solutions to distance learning barriers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".