“Classi in rete”. Rethinking education in small school. An experimental research in Abruzzo
Bibliographic record
Abstract
The school today is organized according to a specific configuration dating back to the sixteenth century and definitively established during the \nnineteenth century, called by Vincent (1994) the ‘dominant forme scolaire‘. However, there are other organizational forms developed above all in rural areas and typical of ‘small schools‘. Studies demonstrate how the adoption of network technology in teaching can better integrate remote students' classroom (Hannum et al., 2009) and out-of-school activities (Hawkes et al., 2002; Panizzon, Pegg, 2007), but it requires a support action for the teacher preparation (Azano et al., 2019). INDIRE, in collaboration with le Centre scolaire du Fleuve et des Lacs (Ministère de l'éducation et de l'enseignement supérieur du Québec), Italian Univeristy Line (IUL), Ufficio scolastico Regionale (USR) Abruzzo and an expert of University of Valle d'Aosta, is engaged in experimenting innovative methods to help small schools teacher to overcome the limits deriving from remoteness (Mangione, Cannella, 2020). ‘Classi in rete, Classes on the net, model originates from the study of a Québec government initiative called École éloignée en Réseau (Allaire et al., 2009) and it is based on classrooms as Knowledge Building communities (Scardamalia, Bereiter, 2010; Cacciamani, Messina, 2011). Delocalized classes aim to design a common disciplinary path involving student groups in parallel in the same activities by adapting calendars, \nspaces and teacher roles. Teachers of delocalized classes share cooperative educational practices such as ‘pairs aidants‘, ‘mentorat‘ or ‘delocalized equipe‘ by using Video conferencing and Knowledge Forum (KF) (Mangione, Pieri, 2019). This study aims to implement a hybrid model (combining online synchronous, asynchronous and face-to face activities) to work with classrooms as Knowledge Building communities, inspired to Classi in rete framework, and to analyse the propensity for change by small school teachers involved in the project as an indicator to evaluate the impact of implementation. It explores the training and experimentation path that involved 12 Abruzzo small schools, 11 digital animators, 31 teachers, 6 observers (school principals). The path recalls MOOC-Eds (Clark, 2014; Kleiman et al., 2015) space were digital animators and teachers able to think about the model, to identify application contexts and themes on which to build twinning between different schools and classes. In this space teachers able to reflect through the construction of ‘notes‘ in the KF and the participation in synchronous meetings with research group, receiving regulative feedback aimed at improving experiences in their classes. The «propensity for change by small school teachers» is identified with reference to the concept of readiness for change, defined by Armenakis (1993) as related to the beliefs, attitudes and intentions of members of the organization with respect to the need for change and the organization's ability to successfully make such changes. Through the application of MESI battery (Moè et al., 2010), and at the same time the qualitative analysis of the focus group made with some of the teachers who took part in the experience, will be possible to understand if and how the participation in the Classi in rete path promotes a change in the Motivations, Emotions, Strategies and Teaching routines of the teachers involved.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".