Sudden Shift to Distance Learning: Analysis of the Didactic Choices Made by Italian Secondary School Teachers in the First COVID-19 Lockdown
Bibliographic record
Abstract
Abstract Following China, the next severely affected country due to the COVID-19 epidemic was Italy. In consideration of the increasing number of infections, the government via the Ministerial Decree (DPCM) of March 2020 established various restrictive measures for the entire Italian territory, even involving the closure of schools. Hence, for the first time, the Italian school system had to adopt distance learning. The mixed methods research in this context involves a non-probabilistic sample of 6,384 secondary school teachers answering a questionnaire issued from 5 August to 1 September 2020, and 30 telephone interviews were conducted among those who had made themselves available during the compilation of the questionnaire to be contacted for the qualitative part of the research. Therefore, the answers collected in the report 1 relay what happened in the second quarter of the school year from 2019 to 2020, the period of the first lockdown in Italy, through which we try to particularly understand the didactic activities implemented by teachers, the main decision maker of the choices, the assessment methods, the autonomy of teachers in managing distance learning and the teachers’ training needs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".