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While researching structured collaboration techniques, I came across some interesting work people are doing. Mindquarry, for example, provides a model of collaboration patterns based on 4 elements – people, productivity software, collaborative software and methods. I had earlier referred to Mindtools, who provide a rich set of structured collaboration techniques, like for example starbusting, which is a form of brainstorming. Also, Value based management offers a host of techniques, models and theories.
Essentially, structured technology aided collaboration techniques are a medium through which learning efficiencies can be increased. These techniques:
- are contextual to domain
- are contextual to collaboration type (say, brainstorming vs voting)
- are open or close ended (in terms of time, scope, boundaries etc)
- could be ad-hoc or planned
- are quantifiable (both quantitatively and qualitatively speaking)
- are historically referenceable (audit trails for recorded collaborations)
- have rules of engagement
- can be structured to the desired level (sequence of activities, organization of inputs, permissions and access roles)
- are sensitive to scale of audience, available knowledge and other physical parameters
- result in trackable outputs/analytics
The logical next step, from a design perspective, is to attempt to model them. Aldo de Moor’s paper on Community Memory Activation with Collaboration patterns yields some insights on what patterns could be modelled. The abstract for the paper is:
We present a model of collaboration patterns as reusable conceptual structures capturing essential collaboration requirements. These patterns include goal patterns (what is the collaboration about?), communication patterns (how does communication to accomplish goals take place?), information patterns (what content knowledge is essential to satisfy collaborative and communicative goals?), task patterns (what particular information patterns are needed for particular action or interaction goals?), and meta-patterns (what patterns are necessary to interpret, link and assess the quality of the other collaboration patterns?). We show how these patterns can be used to activate communities of practice by improving their collective, distributed memory of communicative interactions and information. We outline an approach that structures how collaboration patterns in communities of practice can be elicited, represented, analyzed, and applied. By presenting a realistic scenario, we illustrate how community memory could be activated in practice.
The other key component is to understand what is the need to collaborate and the forces impeding the required collaboration. This is key to understanding whether collaboration techniques shall be used, substituted by informal methods or not used at all. It is important to understand if they are “over sold and under used” or are “methods seeking an application” or are really cost-effective or intuitive. We have seen that in software engineering too and this may require change management to implement in enterprises.
In other words, the challenge is not quite really all about the technology or process, but is perhaps more about the individual mindset and the overall objectives with which structured collaboration techniques are to be implemented (basically saying that a great process or tool does not automatically ensure collaboration that follows the process or uses the tool or format).
It goes back to us, as individuals, and how we collaborate as subjects, alone or in teams or in networks. If the capability to collaborate in structured ways is learnt and becomes “native” so will adoption on a more widespread basis. On the other hand, organizations or learning delivery modalities can include, as mandatory components, such patterns, tools or processes as part of the workflow.
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?
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:
- How does learning occur? – Distributed within a network, social, technologically enhanced, recognizing and interpreting patterns
- What factors influence learning? – Diversity of network, strength of ties
- What is the role of memory? – Adaptive patterns, representative of current state, existing in networks
- How does transfer occur? – Connecting to (adding) nodes
- 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:
- Enables us to recognize and interpret patterns that exist (way finding, sense making) ; indeed, generate our own new patterns
- Helps us build adaptively on and capture existing patterns given a rapid changing core and diverse knowledge sources
- Provides a distributed environment (both for knowledge and people)
- Provides avenues for social collaboration
- Is technologically enhanced to deal with diverse processes/circumstances such as negotiating information overload, self organization, determining order within chaos etc.
- Enables us to leverage and expand on a network that is diverse
- Helps us build ties at varying strengths that in turn may determine the efficacy/effectiveness of our learning
- 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!