Peer Production of Online Learning Resources: A Social Network Analysis

This paper describes methods for collecting user activity data in a peer production educational system, the Instructional Architect (IA), and then takes a social network perspective in analyzing these data. In particular, rather than focusing on content produced, it focuses on the relationship between users (teachers), and how they can be analyzed to identify important users and likeminded user groups. Our analyses and results provide an example for how to select the most important factors in analyzing the dynamics of an online peer production community using social network analysis metrics, such as in-degree, out-degree, betweenness, clique, and community. In this way, this paper contributes both to process and outcomes research.

[1]  Bongwon Suh,et al.  So you know you're getting the best possible information: a tool that increases Wikipedia credibility , 2009, CHI.

[2]  Mimi Recker,et al.  Predicting document access in large multimedia repositories , 1996, TCHI.

[3]  John Scott.  What is social network analysis , 2010 .

[4]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[5]  Barry D. Davidson,et al.  Social networks, communication styles, and learning performance in a CSCL community , 2007, Comput. Educ..

[6]  Diana G. Oblinger,et al.  Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. A 21st Century Agenda for the National Science Foundation , 2008 .

[7]  Alejandra Martínez-Monés,et al.  Combining qualitative evaluation and social network analysis for the study of classroom social interactions , 2003, Comput. Educ..

[8]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[9]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[10]  Tamás Vicsek,et al.  The Critical Point of k-Clique Percolation in the Erdős–Rényi Graph , 2007 .

[11]  David J. McArthur,et al.  From nsdl 1.0 to nsdl 2.0: towards a comprehensive cyberinfrastructure for teaching and learning , 2008, JCDL '08.

[12]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[13]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

[14]  Mimi Recker,et al.  Perspectives on Teachers as Digital Library Users: Consumers, Contributors, and Designers , 2006, D Lib Mag..

[15]  Andrew Laghos,et al.  Social network analysis of self-taught e-learning communities , 2007, Int. J. Knowl. Learn..

[16]  Marshall S. Smith,et al.  The Promise of Open Educational Resources , 2006 .

[17]  Hans-Jürgen Engelbrecht.  The wealth of networks: How social production transforms markets and freedom , 2006 .

[18]  Jock Given,et al.  The wealth of networks: How social production transforms markets and freedom , 2007, Inf. Econ. Policy.

[19]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[20]  Vic Lally,et al.  Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis , 2007, Int. J. Comput. Support. Collab. Learn..

[21]  M. Scardamalia,et al.  Designs for Collective Cognitive Responsibility in Knowledge-Building Communities , 2009 .

[22]  Chrysanthos Dellarocas,et al.  Harnessing Crowds: Mapping the Genome of Collective Intelligence , 2009 .