Identifying sets of key players and cliques in socio-educational co-creative projects

Evgeny Patarakin, Vasiliy Burov, Roman Parfenov

Electronic Governance and Open Society: Challenges in Eurasia (EGOSE 2015)St. Petersburg, November 24-25, 2015

Introduction

• The paper presents a new form of network collaboration – the socio-educational projects as an activity that aims not only to better management, but also the formation of links between actors and objects of collaboration.

• Our contribution makes agent-based model which help identify key actors and biggest cliques of actors.

SOCIO-EDUCATIONAL PROJECTS

• Governance and society needs co-creative projects with a final document as the end output of the collective activities.

• Education needs in a co-creative projects with a XXI skills as the learning outcomes of the collective activities.

• All these needs lead to the emergence of new type mixed projects: socio-educational co-creative projects.

SOCIAL-LEARNING ANALYTICS

• Learning analytics is a collection of methods that allow to understand what is going on in a learning scenario

• Modern socio-technical systems store the full history of all activities. This history can be presented as a record of a chess or go game, consisting of many moves:

• Agent ID| Object ID| Type of an action|

AGENT-BASED MODEL

DYNAMIC DIAGRAM

DYNAMIC DIAGRAM

Different types of subjects and objects of activity

Identifying sets of key players

• Key players are those elements in the network that are considered important, in regard to betweenness centrality of vertex. Top ten key players are determined in NetLogo as

• sublist reverse sort-on [norm-betweenness] users 0 9

Sociogram of the single step of the edge betweenness algorithm

Identifying sets of maximal cliques

• A clique is a subset of a network in which every node has a direct link to every other node. A maximal clique is a clique that is not itself contained in a bigger clique. Cliques containing more than N members are determined in NetLogo as

• nw:maximal-cliques [if (count ?) > N

Regionally caused maximal cliques

CONCLUSION

• We used the technique of dynamic agent-based sociograms to:

• • Trace how and based on what objects forming links between participants of collaborative production.

• • Identify key players and stable biggest cliques, which serve as a cores that support the operation of network communities

• • Analyze the effects of remove of the key players from the field of collaborative production

Identifying sets of key players and cliques in socio-educational co-creative projects

Evgeny Patarakin, Vasiliy Burov, Roman Parfenov

Thanks for your attention