Surviving online learning handbook: what does COVID teach us about online learning in high schools?
Bibliographic record
Abstract
Unlike many disaster scenarios, there was no guidebook for school communities to consult as they wrestled with the ensuing fallout of a global pandemic. This emergency offered educational policy makers a rare opportunity to not only evaluate current attitudes towards online learning, but also discuss the realities and impacts of large-scale educational transitions from traditional classrooms to fully online environments—particularly in K-12 environments. The purpose of this research is to explore the perceptions of online learning within a high school learning community in response to their district’s implementation of online learning following the outbreak of COV-19 to help better inform and shape the future development and implementation of online learning opportunities. This research is designed around a collective case study framework using semi-structured interviews and surveys of students, parents, teachers, and administrators in a midwestern suburban high school starting during the 4th quarter of the 2019-20 school year through the first semester of the 2020-21 school year. Data from these methods was compared and contrasted between cases and emergent themes were then interpreted alongside evidenced trends in recent research in online learning and concepts related to forced second-order change spurred by the COVID-19 pandemic. These shared findings were then organized into suggested survival tips for schools to consider during future implementations of online learning. Experiences from interviews and perceptions from surveys reveal a number of shared feelings in the learning community related to certain advantages and disadvantages of online environments, perceived higher workloads and anxiety, potential factors that help and inhibit success in online environments, and obstacles for students who rely on extra support services. All interest groups agreed that some students thrived in online environments, though many did not. Ultimately, all interest groups largely agreed their overall perceptions of online learning improved over the course of implementation, and a majority of the school community wanted more online opportunities offered to students even when school returned back to normal. Hopefully these findings convince educational leaders to reconsider the promising potential roles of online learning in K-12 settings as school communities inevitably transition back into classroom environments.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".