Cloud computing service for knowledge assessment and studies recommendation in crowdsourcing and collaborative learning environments based on social network analysis

2015 ◽  
Vol 51 ◽  
pp. 762-770 ◽  
Author(s):  
Vladimir Stantchev ◽  
Lisardo Prieto-González ◽  
Gerrit Tamm
Keyword(s):  
Cloud Computing ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Knowledge Assessment ◽  
Cloud Computing Service

scholarly journals A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
Author(s):  
Dongryeul Kim
Keyword(s):  
Middle School ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Middle School Students ◽  
Collaborative Learning ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Science Class ◽  
School Students

  In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

2016 ◽  
Vol 9 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Ángel Hernández-García ◽  
Miguel Ángel Conde-González
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Real Data ◽  
General Purpose ◽  
Computer Supported Collaborative Learning ◽  
Message Boards ◽  
Social Graphs

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

Applying Social Network Analysis and Social Capital in Personal Learning Environments of Informal Learning

2012 ◽  
pp. 75-92 ◽  
Author(s):  
Xiaojun Chen ◽  
Jea H. Choi ◽  
Ji Hyun Yu
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Informal Learning ◽  
Learning Environments ◽  
Personal Network ◽  
Personal Learning ◽  
Personal Learning Environments ◽  
Collaboration Dynamics

Recently, researchers in the instructional technology and learning sciences arenas have started to pay attention to the concept of Personal Learning Environments (PLE). With the aim to investigate how social network theory could indicate the desired indicators for successful Personal Learning Environments, the authors are addressing social capital theory as a conceptual framework to understand the network landscape within informal learning environments. Social capital is an inherent property of network and collaboration dynamics, along with key indicators related to personal network measurements. Personal network analysis as a means to evaluate the social capital is discussed later in this chapter. This chapter is not about learning what or learning as becoming, but about how people learn with whom, and with what degree of influence. It will be helpful to educators or researchers who are interested in measuring academic and psychosocial outcomes within the presence of social capital when applying personal social network analysis in personal learning networks.

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