Fare scuola a classi aperte in rete. Sperimentazione di didattica condivisa in piccole scuole isolate e con pluriclassi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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: <br> Q1 Has the experience of networked classes fostered changes in the teaching practices and strategies of teachers?<br> Q2 Which are the elements that conditioned the teamwork in open classes?<br> 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.<br>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it