Fare scuola a classi aperte in rete. Sperimentazione di didattica condivisa in piccole scuole isolate e con pluriclassi
Bibliographic record
Abstract
In the school year 2020-2021, INDIRE launched in Italian small schools a pilot experimentation of a teaching method already validated in Québec and considered useful for overcoming the educational limits typical of remoteness scenarios (Mangione and Cannella, 2020). “Classi in rete” is characterized by a shared didactic practice where “delocalized” classes are involved in a common disciplinary path by adapting calendars, spaces, and teacher roles, using virtual twinning environments, videoconferencing and spaces for discussion such as the Knowledge Forum (KF) (Mangione and Pieri 2019; Mangione et al., 2021). The experimentation of the model in Abruzzo involved 12 small schools and is based on a design-based research methodological approach (Sandoval, 2014). This paper aims to answer the following questions: Q1 Has the experience of networked classes fostered changes in the teaching practices and strategies of teachers? Q2 Which are the elements that conditioned the teamwork in open classes? The analysis uses a mixed method that integrates a standard search through data matrix and an interpretative search through group interviews aimed at the involved teachers and students. In fact, alongside a structured quantitative survey aimed at understanding the impact that the model had in the experimental classes in terms of collaboration, interdisciplinarity, reorganization of times and workspaces, we conducted a qualitative analysis based on focus groups with the teachers involved aimed at understanding to what extent the model has affected their propensity for change.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".