Social Learning Analytics to Study Teachers’ Large-Scale Professional Networks - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2013

Social Learning Analytics to Study Teachers’ Large-Scale Professional Networks

Riina Vuorikari
  • Fonction : Auteur
  • PersonId : 986864
Santi Scimeca
  • Fonction : Auteur
  • PersonId : 986865

Résumé

A growing interest in research focuses on teachers’ large-scale socio-technical networks. Social learning approaches such as social constructivist theory is well established, however, the current challenges lie in creating reliable methods to gather evidence of how and under which conditions social learning takes place in such socio-technical networks and how does it support teachers’ lifelong learning goals. The field of Learning Analytics (LA) addresses the issue of individual learners, whereas Social Learning Analytics (SLA) addresses that of groups’ processes in knowledge construction.The eTwinning action is used as a case study for applying the concepts of Social Learning Analytics. Our interest is on teachers’ co-operation behaviour and patterns within a socio-technical network and how that can support teachers’ continuous professional development. The eTwinning platform currently hosts more than 160000 European teachers. We first introduce the underlying pedagogical and lifelong learning related assumptions regarding teachers’ co-operation. To better understand the type of activities that teachers undertake in eTwinning, they are classified according to the OECD‘s indices for teachers’ co-operation. This creates the core of the eTwinning Analytics framework, which operationalises activities and allows them to be better measured and monitored. A snapshot of data from the platform is used as a proof of concept.
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hal-01349398 , version 1 (27-07-2016)

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Riina Vuorikari, Santi Scimeca. Social Learning Analytics to Study Teachers’ Large-Scale Professional Networks. 1st Open and Social Technologies for Networked Learning (OST), Jul 2012, Tallinn, Estonia. pp.25-34, ⟨10.1007/978-3-642-37285-8_3⟩. ⟨hal-01349398⟩
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