Sustained Knowledge Innovation Network
Emerging Network
Frequent Idea Interaction Network
Figure 7: Knowledge-‐building sub-‐networks. In all figures, unit of analysis is “contribution,” in which each red-‐circled-‐node represents a participant; each blue-‐squared-‐node represents a view; and each tie represents a note contribution by a participant to a view.
Table 7 summarizes statistics about the four sub-‐networks. The Sustained Knowledge Innovation Network as a sub-‐network has the highest number of note contribution and ties between participants and views and represents a more dynamic network for Knowledge Building. The Emerging Network as a sub-‐network shows the lowest numbers of note
contribution and ties and the highest numbers of inactive participants and views; it is clear that its weakness lies in its low participation. The Intensive Participant Interaction Network as a sub-‐
network has a lower number of active participants and a higher number of active views
whereas the Frequent Idea Interaction Network as a sub-‐network has a higher number of active participants and a lower number of active views. These results suggest an imbalance between participant and idea interaction for the latter two sub-‐networks. Such disequilibrium is
reflected in: (1) strong participation within certain Knowledge Forum views but weak exchange and diversification of ideas between views. A potential issue inherent in such a sub-‐network is that participants may be engaged in social interactions rather than producing and sharing ideas of value to a broader community and taking these ideas to greater depth; and (2) frequent exchange of ideas between Knowledge Forum views but weak participant interaction within
these views. These network dynamics can be problematic for sustained knowledge
advancement because they suggest connections between ideas across problem spaces but lack of strong participant interaction to elaborate, deepen, and improve ideas. Now that strengths and weaknesses of each sub-‐network in the KSN are more explicitly identified, we turn to the matter of how to enhance the design of KSN.
Table 7: Comparisons between Four Knowledge-‐Building Sub-‐Networks.
Sub-‐networks Note
Note: Note contribution is the total number of notes contributed to a given sub-‐network; Ties are the total connections between participants and views in a given sub-‐network; Active participants are participants who contribute at least one note to a given sub-‐network; Active views are views that receive at least one note contribution in a given sub-‐network; Inactive participants contribute no notes; inactive views have no
participants contributing notes to them. Bolded numbers refer to the highest value; numbers in parenthesis refer to the lowest value.
Expanding the Possibilities
This research represents a design experiment and its main purpose is to improve network dynamics within the KSN. To this end, we (1) examined KSN’s network growth over four years;
(2) analyzed network dynamics; and (3) identified potential strengths and weaknesses relevant to participant and idea interaction. The goal was to identify design improvements to enhance sustained knowledge advancement within the KSN. In summary, the KSN’s growth and
sustainability over time, as revealed through our analyses, suggests the possibility of achieving increasingly high levels of knowledge advancement. Analysis of KSN’s interaction patterns suggests two possible means to improve the KSN. The first is to enhance participant interaction between isolated sub-‐communities in the periphery, and the second is to increase idea
connections between more temporally distant Knowledge Forum views (e.g., views created in Year 1 and views created in Year 4). While KSN is moving toward a sustained knowledge innovation network with strong participant interaction and strong idea interaction, there remain a fair number of reasonably inactive participants (n = 108) and views (n = 62) whose connections can be further enhanced.
An important challenge is thus to establish more dynamic, direct, and concurrent connections between participants and ideas of the four types of sub-‐networks identified above, as this is likely to enhance Knowledge Building. For example, literature has suggested that less active participants or peripheral members in a community can lead to strength (Granovetter, 1983).
Sometimes, less active participants in one network/discipline may be active core members in another network/discipline. They represent a potential source of fresh and diversified ideas;
they may be able to bring in new ideas from different disciplines, if their ideas can be more directly linked. Other times, less active participants may be practitioners (e.g., educational or health care practitioners) whose ideas may be of great value in terms of their practical
implications to theorists or researchers if more direct discourse connections can be provided.
Or, less active participants may be simply peripheral participants in a community. So,
establishing more direct connections may be helpful in bringing these peripheral participants (Wenger, 1998) into the culture of core members. Low or imbalanced participant and idea interaction may also have to do with KSN’s increasing network size. While continual growth in the KSN is desirable, it also increases the difficulty of maintaining dynamic and effective participant and idea interactions for continual knowledge advancement.
To address the above challenges and issues it is important to support more direct and
meaningful connections, and perhaps a mechanism to identify who the less active participants are (e.g., whether they are practitioners or new members) and how to engage them.
Our research team is currently adding new design features into Knowledge Forum, including the development of a suite of new assessment tools (Hong, Scardamalia, Messina & Teo, 2008;
Scardamalia, Bransford, Kozma & Quallmez, 2010). One of the new tools is a more powerful Social Network Analysis Tool, which enables members to freely explore existing interaction patterns among participants in the KSN (cf. Hoadley & Pea, 2002; Vivacqua, Moreno & de Souza, 2003). Another tool is the Semantic Analysis Tool (Hong & Scardamalia, 2008; Teplovs, 2005), which allows members to explore idea interaction patterns between views (e.g., what ideas relate to what ideas). Tools for identifying promising ideas should also help bring ideas to the attention of all participants, regardless of whether they were generated by the periphery or core group. At an individual level, the tools are designed to extend members’ social
metacognitive capacity (e.g., knowledge of others’ knowledge, see Hong & Lin, 2008) to support epistemic agency (Russell, 2002; Scardamalia, 2002) for more effective Knowledge Building initiated by the members themselves. A newly created KSN view is being used to elaborate new tools for meta-‐discourse and “big ideas.” This will allow more productive interaction based on ideas rising to a higher plane across views. These new tools should allow members to monitor and reflect more often on who has worked on which ideas (or sets of ideas), so members share a meta-‐perspective on their work. More effectively distributed Knowledge Building processes should result (Hewitt & Scardamalia, 1998).
As Scardamalia (2003a) suggests, "Networks are ubiquitous, but the social engineering of networks for effective action is in its infancy" (p.63). The importance of this study lies in its possible contribution of new knowledge to our understanding of social processes and of how
for knowledge advancement. Specifically, this study expands understanding of the strengths and weaknesses of four possible network models and of how these models can be mapped onto the structure of the KSN to increase self-‐organizing innovation dynamics (Rycroft, 2003).
Our society is increasingly organized around networks. Having the know-‐how and capacity to design more innovative networks for a more creative knowledge society has become
increasingly important (Gloor, 2006). To address this societal concern, our study provides an initial, overall look at four different network models, and used these models as an analytical tool to examine the network structure of the KSN. For future research, it should be fruitful to further explore the social dynamics within each specific network model, compare network models, and investigate how a network model evolves over time. Doing so would help to explain the complex network phenomena in the KSN and to continually improve its design to support sustained Knowledge Building.
Acknowledgments
This research was funded by an Initiative on the New Economy (INE) Grant from the Social Sciences and Humanities Research Council of Canada (512-‐2002-‐1016). We owe special thanks to all participants for their contributions to the Knowledge Society Network.
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