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Posts Tagged ‘complexity’

Anarchist Curricula

What if teachers treated teaching as an extension to research? What if teaching was really enculturation of students into a field of inquiry? What if teachers were to engage in discovering new insights with the help of their students, activity by activity, day by day? What if this co-research also included the additional studies of the meta-narratives of student progress and performance, as truly as of their own?

Curriculum, interaction and progress would take on very new meanings altogether in this paradigm. No longer would the syllabus be a linear, hierarchical assembly of reductive learning objectives. Nor would assessment itself be linear and deterministic. Knowledge would constantly be co-created and emergent. Feedback would be harnessed. It would become commonplace to publicize advancements, to celebrate opinion, to demonstrate the ascendance on forever new plateaus and to be reflexive, aware thinkers and do-ers.

So too would get removed the barriers between disciplines, the confines of grades and the tyranny of the score. And in their place, would flourish an emergent, self-organizing and complex adaptive system.

The complexity-based curriculum would be dynamic, emergent, rich, relational, autocatalytic, self-organized, open, existentially realized by the participants, connected and recursive (e.g. Doll, 1993), with the teacher moving from the role as an expert and transmitter to a facilitator, co-learner and co-constructer of meaning, enabling learners to connect new knowledge to existing knowledge. Learners, for their part, have to be prepared to exercise autonomy, responsibility, ownership, self-direction and reflection.

Learning is dynamic, active, experiential and participatory, open-ended, unpredictable and uncertain, and cognition requires interaction, decentralized control, diversity and redundancy (Davis & Sumara, 2005). Emergence and self-organization require room for development; tightly prescribed, programmed and controlled curricula and formats for teaching and learning, and standardised rates of progression are anathema to complexity theory. It breaks a lock-step curriculum.

Educational Philosophy and the Challenge of Complexity Theory, Keith Morrison, Macau Inter-University Institute, in Complexity Theory and the Philosophy of Education, ed. Mark Mason, Wiley-Blackwell, 2008.

For many generations now, the focus on reducing assessment to a set of verbs (started by Bloom et al in 1956), reducing learning to achievement of a set of outcomes contained within tight disciplinary boundaries and graded progression by age, as well as theories of learning that have framed and informed teaching and assessment, have led to a deeper focus on the what and how of content, assessment and teaching, rather than the why, where and who.

Education and educational research conceived in terms of expanding the space of the possible rather than perpetuating entrenched habits of interpretation, then, must be principally concerned with ensuring the conditions for the emergence of the as-yet unimagined. We would align these suggestions with Pinar and Grumet’s (1976) development of the notion of verb currere, root of curriculum (along with a host of other common terms in education, including course, current, and recursive), through which they refocused attentions away from the impersonal goals of mandated curriculum documents and onto the emergent and collective processes of moving though the melée of present events.

Complexity as a theory of education, Brent Davis and Dennis Sumara, University of British Columbia, Canada

There is now an anarchist epistemology available – that questions the relevance of the existing paradigm in a world that is increasingly being recognized as complex and adaptive.

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There is a teacher in everyone of us. It is useful to acknowledge that a whole lot of things are learnt without someone actually teaching us, and that perhaps someone is right now learning from us without our even knowing it. On the Internet, this is possible at a very large scale. We learn from other people’s review of the computers we buy or the places we visit. We learn to dress by looking at what others wear and talk as we hear others speak. We learn from reading a blog post or the fact that a guru likes a particular URL or that an expert just followed an innovative startup’s twitter handle.

So when practicing teachers and real experts, who really do all of this teaching and coaching professionally, start making their actions, their learning, their idiosyncrasies public, a whole lot of people will end up learning even if they are not in their class. Perhaps their class will also learn much more if they share the guru’s network, the guru’s learning trails across the World Wide Web.

As teachers, it is really about how we learn and how we share how and what we learn. It is not learning how to use technology (which is an important enabler, but not an end in itself), but how to embrace a culture of open-ness, sharing and a much heightened consciousness that we are professional performers of a learning process; that as teachers we are actually enacting the role of expert learners.

For that, we have to re-envision the way we learn. We are a product of much the same system that we subject our children to. We bind our students by its same constraints. We are steeped in the routines that we have perfected in years we have taught the same curriculum again, again and again. We cannot change ourselves by thinking in the same ways the system has taught us. We must re-envision our own futures, standing outside the systems of today.

Why it is so phenomenally important to re-learn how to learn in today’s networked environments? Its possible because, invariant to scale, the network has opened up hitherto unknown opportunities to teach and learn. Not that you can now learn something that was previously hidden from you, but that you can now learn and teach in ways that may be much more than the classroom we are so used to. In fact the classroom analogy does not even exist in the networked environment (the closest it gets is “clusters” or “swarms”) – the network is not a class.

Since networks are not classes, you cannot apply traditional teaching-learning techniques to it (or atleast not as-is). So an entire paradigm becomes near-obsolete when one thinks of networked learning. Which is not what the xMOOCs would have you to believe, but that is entirely their loss.

If you can think network, you can break away from the traditional mode. It is what we must do. Case in point. If there is no class, who are you teaching? Answer: You are teaching a cluster of nodes (students) bound to you in some manner (through your institution perhaps), but they are really part of many different networks as well. By connecting to those students and promoting transactions between them, helping them add new connections to their network, and leveraging their existing networks, you will build upon a fabric of learning, much like a weaver or an Atelier. You will help them break away from the monotone of traditional systems, help them celebrate chaos and let them build their capability to learn.

When you become that networked teacher, you will contribute to a scale of learning that will be unbelievable. What you will do within your own small networks, may become amplified or contribute to global knowledge about learning and teaching. Just the sheer scale of your teaching and learning, your networks, the types of interactions, will fast transcend the power of any certificate or degree the traditional system may have to offer.

The revolution is here. It is you. Seize the day.

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In 2008, when discussing the critical role of technology in the existence of a Connectivist learning environment, Stephen commented:

Take the steam engine, for example. It works through a process of burning coal to heat water, which turns to steam, which it then fed through a turbine or engine in order to produce power.

You need quite a but of technological infrastructure to build the engines, and you need coal to burn. Without the technology and the coal, you don’t get the benefits of the steam engine.

Does this mean that the principles behind the steam engine are not generalizable? Of course not. The *principles* apply to everybody, whether or not they have a steam engine.

It’s the same thing with connectivism. The principles apply, even to people who do not have the technology to easily observe them in action.

Again, at EDGEX in 2012 (at about 28 minutes into the discussion), George Siemens asserted that without technology, a lot of the abundance of knowledge, which is one of the fundamental reasons for Connectivism (and that itself requires more technology to interpret and access), would not be exposed to a large number of people. Stephen Downes made the point that networks (that have existed even without technology) are the underpinnings of a Connectivist environment and that technology facilitates the making of connections (for example, the Six Degrees experiment).

This is important for India, and for others at our level of development. The technology required to make globally diverse connections simply does not exist for a large number of people. A large number of people simply are not overloaded by the abundance of information, most do not even know that there is more to knowledge than the community or place they live in.

At low levels of development, perhaps the small local network is all that exists, constrained by things such as customs/traditions, economic & social power and access to educational opportunities. This is also where the bulk of the population is in countries such as India and large parts of Africa. The conditions for access to technology (computing, internet) are yet to be established in these areas and where they are available, reliability and sufficiency is often a constraint (what do you do if the school does not even have power).

What then, are the ways in which Connectivist learning environments could be designed for this audience without an initial reliance on computing and Internet technology (perhaps including even power)? The most pronounced impact would be speed and immediacy.

Let us look at the factors influencing network formation.

Local Infrastructure & Economic Development levels: Safe assumptions about available local infrastructure could be that there is a way to communicate through telephone (more or less) and postal networks, that power is unreliable and that the Internet availability is meagre. This is especially important for remote areas. East and North-east have the lowest tele-density and just 1% of rural households have a computer with an Internet connection (8% of urban households) [Census 2011 report here]. The report also states that “(o)ne-sixth of the country, or 200 million Indians, don’t possess any of the most basic assets like a transistor or TV, phone, vehicle of any kind or a computer.”

Society, Religion & Culture: Networks are influenced by culture. Somewhere there must be an understanding of local culture and its influence on learning networks. Traditions apply barriers to networks (social constraints) that impacts the ability of the network to grow and diversify. Community bonding, existence of sub-communities and influence centers (including religious) are all important factors.

Language: Language homogenizes and localizes. This has a direct influence (especially in India which is a single country with many languages) on how networks are able to grow.

Population Density: This is an important one. Census Data shows a remarkable skew across states (an average of 382 people per square kilometre, half of Indian states below that with Arunachal Pradesh at as low as 17 and Delhi/NCR as high as 11,297!). This is critical because high density areas throw up many more opportunities (sparks) for networks (fire) creation and growth.

Interlinkages with other communities: For ideas to spread outside the local community, we must look at the transaction points of this community with the external communities. This could be commercial trade, medical & other services or unifying through administrative departments.

Out of these (and many possible others that influence network formation and growth), Connectivist learning environments would focus on ones that they can influence in some way. For example, in an offline scenario, can postcards carrying a question to an expert outside local boundaries be an admissible innovation? We also need to look at mechanisms underlying complex systems to see which interventionist approaches will work better. More on this in subsequent posts.

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The discussion on what is a MOOC or how do we classify MOOCs is gaining momentum. First we had George explaining the difference by saying that there are xMOOCs and cMOOCs. Now Lisa Lane has come with a different taxonomy (network/task/content based) with some interesting distinctions. Dominic came up his own understanding of the “features” of a MOOC. See also Gordon Lockhart’s Super-MOOCA MOOC by Another Name and a brilliant post by Doug Holton, where he makes many insightful remarks including what could be necessary and sufficient conditions for learning to occur or to be “caused” (don’t particularly like that last word).

Taking Doug’s cue, we should perhaps be talking of massive in the sense of the quantum of connected-ness or connection-richness, or in terms of the widespread nature of the learning need or motivation, rather than looking at it from the point of view of number of learner enrolments.

That said, I would reiterate that we are comparing apples with oranges, and despite the “mania”, there is no reason why we should be forced to compare these different initiatives in the first place. MOOCs (cMOOCs) will have a plethora of possible implementation strategies and techniques. For example, I love what the folks at the Mechanical MOOC are doing (Audrey covered them here).

In my opinion, it makes more sense to focus on the platform rather than the tool, the rubric rather than the assessment and the DNA rather than the you or me.

A video, by Prof. John Holland (University of Michigan) speaking on Modelling Complex Adaptive Systems, is a must view (rather long, but worth it) for a large number of reasons. I find this CAS video (and generally the complex systems area) appealing because it makes more sense to me than engineered closed systems like we have in education today.

I am intrigued by the emphasis in the talk of building blocks, signals, interactions and boundaries within an overall approach of risk taking innovation. I think that fundamentally describes the platform I am referring to. Let us look at that process.

When a learner first starts out, certain pre-conditions exist. These pre-conditions are what makes a person a learner – whether it be out of curiosity, awareness, context, a need and/or some other kind of motivation trigger. At this point, the learner understands little of the network of knowledge, and perhaps may also have a sense or purpose or general idea of outcomes from the forthcoming experience. The platform will have to recognize this initial state.

Next comes a series of interactions in and with the network. This is where the accessibility, quality and depth of the network (in terms of coverage, accuracy, engagement, open-ness) and the contained boundaries play a big role in facilitating or obstructing discovery, experimentation and conjecture – viz. sense-making.

The network really is two things – one, an explicitly curated or visible set of people, content and tools, and two, a vast hidden implicit network intimately connected with the first but not explicitly visible at first.

Interaction in the network will be governed by signals – actions by the learner, actions by others and changes in the network itself as it evolves and adapts. The learner will interact to implicitly or explicitly “produce” or “engineer” make visible or personal, a set of connected nodes in the network (which shall be her curation arising out of her discovery, experimentation and conjecture).

The visible and invisble impact of her sense-making and of others will generate fresh signals in a non-linear manner. Over time, some of the network constellations will get broken to form new bonds (or connections) as the process will be usually far from equilibrium. Visible parts will become a part of the network thus changing the network maps of sense-making of others and in turn generating new innovations and experimentation.

Again over time, feedback from these interactions or signals will reinforce collections or patterns of these nodes of sense-making and new building blocks of comprehension and sense making will emerge. This is turn will affect boundaries of interaction and reduce impedance caused by them, so that new constellations are created.

The platform will have to recognize this elaborate dance of sense-making, the signals, interactions, boundaries and complex adaptation. It will have to provide for this complexity and it will need to allow for contextual influence to align towards certain constellations (and it will do so in many ways, giving us the agency). 

The platform will have to recognize and help resolve multiple trails that coalesce into a conception, parallelisms or multiple patterns of building blocks that converge into a model (a thought, an idea). And the system will have to recognize transition or inflection points, when existing models are questioned and new trains of thoughts emerge, just like in this post.

The platform has to provide for this emergence, chaos, self-organization and adaptation. Something that is spectacularly different from what Khan Academy or Coursera or other non-MOOCs are attempting to do. And in doing so, it will forge a new understanding of what an educational system ought to be.

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