Identifying sets of key players and cliques in socio-educational co-creative projects
Download as PPTX, PDF2 likes368 views
The paper discusses the development of socio-educational co-creative projects, emphasizing the importance of collaboration among participants to better management and education outcomes. It introduces an agent-based model to identify key players and maximal cliques within these networks, utilizing dynamic agent-based sociograms and centrality measures. The study concludes by highlighting the implications of key player removal and the structure of collaborative production communities.
![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](https://image.slidesharecdn.com/wikivoteegose2015a-151128142940-lva1-app6891/85/Identifying-sets-of-key-players-and-cliques-in-socio-educational-co-creative-projects-9-320.jpg)
Identifying sets of key players and cliques in socio-educational co-creative projects
- 1. 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
- 2. 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.
- 3. 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.
- 4. 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|
- 8. Different types of subjects and objects of activity
- 9. 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
- 10. Sociogram of the single step of the edge betweenness algorithm
- 11. 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
- 12. Regionally caused maximal cliques
- 13. 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
- 14. Identifying sets of key players and cliques in socio-educational co-creative projects Evgeny Patarakin, Vasiliy Burov, Roman Parfenov Thanks for your attention
Editor's Notes
- #6: The combination of agent-based modeling and network analysis can increase the potential of complexity-based policies
- #7: The use of NetLogo allowed to present the collaboration as a dynamic diagram in which each actor can interact with each other tens of thousands of actors
- #8: The use of NetLogo allowed to present the collaboration as a dynamic diagram in which each actor can interact with each other tens of thousands of actors
- #9: The created model used NetLogo features such as breeds and agentset. NetLogo allows you to define different breeds of turtles and breeds of links. Once you have defined breeds, you can go on and make the different breeds look differently and behave differently. We have used various breeds of agents to separate the subjects and objects of activity, as well as to separate the actors into different classes
- #11: The single step of the edge betweenness algorithm consists in the computation of the edge betweenness for all edges in the graph and in the removal of those key player with the highest score.
- #13: Detection of of large cliques indicates that cooperation between users is performed. We detected that in the company crowdsourcing environment the maximum cliques are formed entirely on a territorial basis