What inherently constitutes a connectivist learning ecology? What specifically differentiates it from a collaborative, Web 2.0 or informal learning enabled learning environment? Was the CCK08 course representative of the Connectivist learning ecology?

Lisa Lane wrote a list of recommendations on the CCK08 experience. Bradley Shoebottom has devised his own structure. I proposed the concept of Network Based Training. There are many others.

George Siemens writes:

I like the idea of thinning our classroom walls and allowing connections to be formed between concepts from other subject areas. But that responsibility shouldn’t rest on the educator. “Getting on the same page” (author’s words) seems a bit at odds with opening up class rooms. We need to all get on our own page, form our own connections, our own understanding of different fields. It seems that the desire still runs high for educators to apply increased organization when problems become intractable. What is really needed is a complete letting go of our organization schemes and open concepts up to the self/participatory/chaotic sensemaking processes that flourish in online environments.

Monty Paul ties in connectivism into social constructivism.

The idea of connectivism (Drexler, 2008 ) ties in well with social constructivism, demonstrating how new generation learners use the power of our networked world to tap into remote sources of knowledge, including experts in various fields. These learners work in a world without boundaries from a technological point of view. They are adept at finding, storing, managing and sharing information using new web-based applications. More importantly, they are involved in knowledge creation, using blogs, wikis and other on-line applications to mash and developing new ways of looking at and using information. These students bring fresh challenges for learning institutions across the educational spectrum, given their need for a fast moving, game oriented learning (Pensky, 2001) which traditional learning environments are hard pressed to provide.

But I want to discuss the difference between the creation of an ecological blueprint (if there could be one) that “allows” connective learning and what would constitute an ecological blueprint that “is” inherently a connective learning design/blueprint. For example, the difference between saying “the hotel lounge is Wifi-enabled” is different than saying that “I can check my email in the hotel lounge”. After all, it’s the conversation rather than the blogging tool that’s more important, right?

George Siemens contrasts behaviourism, cognitivism, constructivism and connectivism in the light of Ertmer’s and Newby’s five definitive questions to distinguish different learning theories. For connectivism, he states:

  1. How does learning occur? – Distributed within a network, social, technologically enhanced, recognizing and interpreting patterns
  2. What factors influence learning? – Diversity of network, strength of ties
  3. What is the role of memory? – Adaptive patterns, representative of current state, existing in networks
  4. How does transfer occur? – Connecting to (adding) nodes
  5. What types of learning are best explained by this theory? – Complex learning, rapid changing core, diverse knowledge sources.

(George’s responses in italics)

George and Stephen also talk of the impact of chaos theory, self organization and complexity on the learning process. They also refer to the impact of this way of learning on traditional notions of power, control, validity and authority (among others). So what would constitute the learning ecology that is connective? It should be one that inherently:

  1. Enables us to recognize and interpret patterns that exist (way finding, sense making) ; indeed, generate our own new patterns
  2. Helps us build adaptively on and capture existing patterns given a rapid changing core and diverse knowledge sources
  3. Provides a distributed environment (both for knowledge and people)
  4. Provides avenues for social collaboration
  5. Is technologically enhanced to deal with diverse processes/circumstances such as negotiating information overload, self organization, determining order within chaos etc.
  6. Enables us to leverage and expand on a network that is diverse
  7. Helps us build ties at varying strengths that in turn may determine the efficacy/effectiveness of our learning
  8. Enables us to negotiate complex learning needs

Replace “what would constitute a connective learning ecology?” with “what kind of educator would suit or engender a connective learning ecology?” and it becomes easier to think about the problem instantly.

That is, the answer that the educator should “model” and “demonstrate” his connective learning process/ability/efficiency while the learner should “practice” and “reflect” (I think “observation” and “experimentation” are equally critical skills), makes sense because the ability to do all of the above needs to be learnt by the learner. The objective is perhaps that the learner be empowered with the learning skills and ecologies of the educator (as George Siemens says “…A curator is an expert learner”).

What if there was no educator or formal role for one? What happens in that truly open, autonomous, distributed, uncontrolled network? Is there an ecology for the solitary learner; for the ones that are faced with unequal access; those who have technological/social barriers or limitations?

In a sense then, perhaps we should look at the design and metrics of a connectivist ecology from a different lens altogether – where the ecology contains components that inherently propel the learner to become a curator.

Instead of providing a chat tool and a structured interaction and participation schedule, it should provide (for lack of a better technologically unchallenged term) a “default” mechanism for learning that propels the learner to make connections, practice and reflect, observe & build & recognize distributed knowledge patterns.

It is here that the discussion around types of networks becomes really important. At the neural level, it is really immersion into the environment (“increased awareness?”); at the conceptual level it is the ability for the ecology to provide some ways of exploring and building concept patterns and at the social level, the learning network of people (and devices) itself in a given context. It is here also that we should perhaps attack the concerns around motivation and participation.

Perhaps when the three (and there may be more) types of networks come together in some way, they become really powerful for learning. For example, experiencing rain-drops, recognizing the dark cloud visual and listening to the thunderclap, associating it with concepts of cloud formation and effects of rainfall, and, warning your friend not to venture out, may be an example of learning could manifest itself given this three way association (there could be self spiraling associations within a network type itself).

Where would the metrics then come in and how would they be designed? In another corporate context, I once read a powerful article by John R. Hauser and Gerald M. Katz titled “Metrics: You are what you measure!”. In my mind and as they state, successful metrics are good if the actions and decisions which improve the metrics also improve the firm’s (read “learner’s”) desired long term outcomes (read “learning ability” or “expertise”). They list seven pitfalls of metric design and how these can completely subvert the metric design exercise. They also list an equal number of steps to design good metrics such as “Listen to the customer” and “Understand the inter-relationships” all of which I think are useful ways to think about what to avoid and what to follow.

The main point is that we need to understand if score, time elapsed, distance between two nodes (a.k.a. social network analysis), e-portfolio submission & ratings et al are good metrics in this connectivist ecology. Instead, wouldn’t we ask questions relating to or perform investigation into how well the learner is able to learn using the “default” mechanism I referred to earlier? For example, speed of learning could be perhaps (or maybe I am being too simplistic) the rate of change of new patterns, network connections, conversations; or the measure of expertise would be the number, qualitative rating, network perception or rate of interaction between you and the resources in your network?

As always, would love to be corrected and to know your thoughts!

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