Agility in Teacher Training: Distance Learning During the Covid-19 Pandemic
Bibliographic record
Abstract
The outbreak of the Covid-19 pandemic forced the world to respond in new and unconventional ways. Quick thinking and unusual flexibility were required whilst operating under conditions of uncertainty and fear. This article deals with agility in the implementation of distance learning during the Covid-19 pandemic as it occurred at Ohalo College of Education with the outbreak of the epidemic in Israel in March 2020. Within 48 hours from the moment that Israel’s government announced a nation-wide lockdown, the College shifted from frontal teaching and learning to social distancing and distance teaching. The College adopted agile leadership that led to moving 700 courses to distance learning and teaching, with 150 lecturers and 1,500 students in their homes; the semester continued, but differently, in light of the lockdown and limitations ordered by the government. It is clear that such swift organization, executed with maximum flexibility, did not benefit from proper planning and was far perfect. This article offers a look at academic agility as demonstrated during the transition of a college of education to distance learning under emergency conditions. It will be examined through an analysis of survey responses from students. The goal of the survey was to assess students’ attitudes toward the implementation of this strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".