As I read the paper on Connectivism, I found myself circling phrases, time & time again. As indicated, the world has shifted significantly, when “ GNP of virtual games (is) exceeding the GNP of many countries”. “Instead of approaching learning as schematic formation structures, learning is the act of recognizing patterns shaped by complex networks.” Yes!!
He reviewed the invention of the printing press, when “access to books was simply a conduit to the higher goal of learning and knowledge”. What is interesting is that he describes the spoken/dialogue form of education during Socrates/Plato as fluid in nature, and compared that to the books/text as object or static in nature. In essence, he reflects that the internet allows us to return to the fluidity of conversation and; therefore, learning outside of space and time.
I loved this calculation from Liebowitz in 1999:
data (+relevance +purpose) = information (+application) = knowledge (+intuition +experience) =wisdom
He suggests, as do others, that “due to rapid growth of knowledge, the act of learning has shifted from acquisition to assimilation, from understanding of individual elements to comprehending an entire space, and thereby, understanding how elements connect”.
I agree completely, from an IT standpoint. Years ago, analysts learned more & more year by year, expanding their zones, until they were highly skilled, fitting the cognitive / pragmatism scenarios. High levels of specialty and access make it impossible for the standard form of developing learning to continue, especially when systems run 7×24 with high availability.
For knowledge growth to occur, I was interested to see that Stephen Downes (2005) suggested that connective knowledge, which has four traits: diversity, autonomy, interactivity and openness. This plays out in system problem resolution, when it is essential that nodes in the network engaged fit those categories.
My challenge in reading this paper, and others, is that it still is missing of the criticality of the selection of those nodes. In order to do so, one must have strong knowledge of such characteristics of the nodes, such as “subject”, “wisdom” , “availability”, “localization”.
Ø Nodes, using the “wisdom” trait may be defined as “brilliant”, “experienced”, “average” or “dangerous”. Dangerous would be defined as someone who will answer a question incorrectly while suggesting they are knowledge in the area.
Ø The availability characteristics could include such sub-traits as “time zone” & “load”.
Ø The localization characteristics might be sub-traits as “region”, “language”
Within informal multi-node networks, there are multiple duplications of nodes.
Example: When a problem occurs in a complex global ERP system, an individual with significant connective knowledge intuitively knows which nodes to engage. As the problems are analyzed, nodes may be released from the analysis, or engaged, based on shifting requirements and regional time zones/availability.
The learning is taking place within the network, yet the ability to learn, and hence, resolve is based on the knowledge of the network and the nodes.