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Inundated by familiar arguments regarding open content, debates on re-use, freemium business models for open content publishers, moral and economic arguments for open textbooks and so on, by David Wiley at #Change11, I can’t help but ask – Is Content King?

Content is king for publishers, authors and institutions in the educational context. This is because, as equated to a textbook or interactive digital learning object, it represents structured interpretations of the domain, vetted through a process of academic scrutiny, and backed by the repute of the author. Often, it degrades into corrupt practices at the institutional level itself, but broadly users and subscribers to the content ascribe value to these outputs, both economic and academic. With open content, there is also a moral (access, freedom) argument. Like other things – LABs, classrooms, teachers, software systems and libraries – an institution treats such content as an essential foundation of learning.

Over time, with the digital variants emerging on new devices, the representation of content in educational contexts has also evolved, but the essential structure remains the same. It is perhaps apt to question the importance we give to the medium at this point. The textbook, as a constrained medium – as an imposition of structure on a non-linear learning process, as an output of decisions regarding the finiteness,  as a representation of the periodicity of the “semester” or the “term”, tied inextricably to the concept of the educational system, predicative of the level of learning & intellectual advancement – in effect, removes the conversation from learning and constrains the learning process in many other ways. It is also fairly impervious to context – both learner and environment. It is an attempt at standardization with personalization left to the wiles of the unsuspecting and often ill-equipped teacher. Wiley himself acknowledges the reusability paradox – the more context laden (read “richer”) a piece of learning content, the less it can be reused.

Taking a medium like this and making it open is as anachronistic as the first generation of eLearning – converting the textbook into interactive digital variants. So long as we consider the textbook as the foundation, we are condemned to operate within its constraints.

Stephen proclaimed the “end of paper” as a threat to the open content model. However, both the claims – of open content models and of the end of paper – are severely located in the context of these developments. It is easy to get carried away and ignore the main problems – that most of the growth in population of learners in the world is not going to happen in these contexts, which are already tanking on GER (Gross Enrolment Ratios). “Online” is still the preserve of the developed nation. The low cost tablet does provide a feasible alternative to the delivery challenge in other nations. The problem is that these other nations are swept away in the hype and perpetuate open content promises to an unresponsive or simply “unable” audience.

Wiley himself provides a possible solution, that of OSOSS (online self-organizing social systems). The “noise” in such systems often puts real world academics off, as also the debate between “academic and everyday knowledge”. But such systems are, by definition, complex systems. The only discordant note to me is the use of the word “online”, as the most relevant prefix. We have to investigate models where online is the most efficient possibility, but other models of conversation exist and are promoted. It is like going back to CCK08, when I asked the stupid question – what would happen to connectivism if the technology did not exist?  Can we think of a paradigm where the poor get richer than a model where the rich get richer?

Content is not just textbooks or eLearning courseware. But somehow, there is a lack of imagination (or perhaps we are still not that state of art), in conceiving options beyond these delivery-oriented keywords. Sure, there have been a lot of initiatives (like the MOOCs) that attempt to break this paradigm, and I hope they succeed in bringing the complexity perspective into education.

To really leverage open content, we must break away from the constraints of the textbook or the eLearning course. We must encourage diversity. For example, my idea of open content would be to take a concept and open it up to the entire world to write their interpretation of it. By implication, the context richness would provide many opportunities for non-linear real life learning. So instead of looking at content vertically (hierarchies of domain trees and curricula), publishers would look at it in a networked manner with clusters of self-similar nodes. In that situation, learners and teachers would both find it instantly easier to locate in-context learning content. In one stroke, then, the reusability paradox would also be resolved, simply through scale.

No real-world system today looks at content that way. The same way for search. If I want to learn about the reusability paradox and I respect David Wiley and Stephen Downes for their seminal ideas around it (top two articles on Google?), I should be able to access the network of content in and around their contributions, plus curations. So I am not searching a digital repository for keywords that an IMS standard predicates, but I am able to put a filter through nodes that are not content items to get to the content I need. And what if there was really an offline way of doing this, so that more and more people could learn that way?

In summary, please let us think out of the box and in a global context.


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!

I read a series of contributions by Stephen, George, Pontydysgu, Attwell, and reviewed PLE diagrams and Wiki entries. George makes the point that PLEs are antithetical to existing educational systems, which are really  structures of power, accountability and control based in a sociological context, not focussed on learner needs and goals.

For this reason, PLEs, which are based on learners needs/goals and concerned with individual and personal autonomy and learning, cannot move into the center of the learning process until the underlying power relationships change.

Attwell suggests that the next steps in research and development of PLEs should include

  • examining in depth how individuals are using computers for learning in different settings (especially non-educational technology) and outside the setting of formal educational programmes;
  • exploring the relationship between informal learning and formal learning in developing competence;
  • examining different forms of competence and how educational technology can support such competences;
  • examining the use of different social software applications for learning;
  • examining in depth the nature and form of computer mediated interactions between learners in different communities;
  • examining the implications of persistence of data for Personal Learning Environments;
  • examining the different ways in which learners might wish to represent learning (both formal and informal);
  • examining what materials are used for informal learning and how they are used;
  • exploring the implications of changing forms and patterns of learning for educational institutions;
  • exploring ways of representing and patterning learning activities interactions;
  • exploring ways of utilising different services – both within and between institutions and with broader communities – to support PLE-type activities;
  • exploring issues in standards and interoperability to facilitate PLE-type development;
  • exploring how PLE-type applications and services can be integrated or work alongside existing educational applications and services

These are important inclusions. For example, Attwell talks about standards, interoperability, nature and forms of computer mediated interaction and data persistence, which are all going to be important for toolmakers and designers of PLEs.

Pontydysgu talks about the inherent contradictions in capitalist societies (and the associated systematized superstructures of education systems) and the power of individual & collective agency (as a transformative agent).

It is also important to reference Lisa Lane’s strategy for change in this context:

The solution is subversive application of connectivist and other useful learning ideas within the current structure, an insurgence for the purpose of fostering emergence.

The discussion begs two questions:

  1. Apart from shifts in underlying power relations, are there any other enabling factors for the adoption of PLEs (as tool/process/concept)?
  2. Is the mode of change that is required “insurgent” or “revolutionary”?


This is Paper 1 for CCK08. I must confess that I did feel that this was a topic best left until the very end of the course J. I am going to treat it as a place to pose the questions that I have so far, perhaps expose the gaps in my reasoning and hope that the ensuing discussion and feedback will help me understand the concepts better.


We communicate differently than we did even ten years ago. We use different tools for learning; we experience knowledge in different formats and at a different pace. We are exposed to an overwhelming amount of information—requiring continually greater levels of specialization in our organizations. It is here—where knowledge growth exceeds our ability to cope—that new theories of knowledge and learning are needed. And it is in this space that a whole development model of learning must be created (i.e. learning beyond vocational skills, leading to the development of persons as active contributors to quality of life in society).
[Connectivism: Learning Theory or Pastime of the Self-Amused? George Siemens, 2006]


It is here that I have some questions and concerns to start with. To me this represents a generalization at many levels –


  1. what is apparent to a privileged few that have access to technology cannot necessitate new theories of learning and be generalized to everyone who do not – the very concept is inherently hegemonic and ignores the vast majority who are expected to catch up when they can negotiate the great divide
  2. the very concept of information overload and knowledge growth exceeding our ability to cope is made prophetically self fulfilling by the very technology that enables it
  3. the presumption that these principles hold for everyone and all learning, like the principles behind a steam engine
  4. do we have any historic precedent (massive technical/social change) that by itself necessitated a new way of looking at knowledge and learning? Has ever been knowledge growth been that small so as not to exceed our ability to cope?
  5. what knowledge is growing obsolete so fast that it is necessitating a new way of thinking about knowledge and learning? What knowledge is doubling every 18 months? Is it at the K12 level? Under-graduate? Organizations only? Specific domains?


Further, I think it is important to acknowledge that:


a)      it is not necessary that the information explosion equates to an explosion in knowledge that results in actionable learning i.e vast amounts of information is irrelevant to the context of an individual’s learning needs or redundant/duplicated and/or useless in a given context or subject area,

b)      it is not feasible to assume that this apparent information overload that has resulted from the access to new tools to collaborate and publish did not exist before it was digitized in blog posts and YouTube videos (that would be to say that there was no conversation, and worse, no experience and no evolution of thought);

c)      conversations have a multiplicative quantitative effect where people are concerned (take gossip) thereby exponentially increasing any quantum of information


Secondly, I feel Connectivism does not provide me with any insights yet of how I would build a working model for a new educational milieu. Of how to directly address some of the crucial logistics challenges facing nations, societies and corporations alike – how to equip or enable to self-equip millions of people, given time delimited goals, in a fairly reliable manner, with the skills to ensure a livelihood or perform a set of tasks or build competency in an area of strategic national/institutional vision; or how to identify and reward the best performers in an organization; or how to ensure that learners reflect and engage with diversity etc. Is there a formal methodology that can be derived from the theory that provides these results?


The third problem is around externalization vs internalization in the comparison with other theories of learning. How can I write this blog post without internalizing a series of articles that I have read and podcasts I have heard and conversations I have had, without performing several dialectical cartwheels and then presenting my thoughts? Externalization is a means of expression, an output of things we have already made sense of before we put pen to paper, brush to canvas, finger to keyboard….that intelligence that we CAN express or demonstrate in some form. That externalization can be part of a process of several exercises in sense making, often through collaboration. The internalization that must precede it could and perhaps should leverage my network of resources to be maximally effective or productive for me.


The fourth problem that I face in comprehending Connectivism or rather what really disturbs me, is the adaptation of the concept of a network from network technology as it exists today. So also the attempt to bring AI generation/approach 2 as a potential approach for modelling the learning process and knowledge itself.


Actually it is not the analogous use of the concept as much as the direct derivation from and the dependence on technology that shapes the discourse itself (e.g. star and mesh topologies, balanced load, density, single point of failure, recommender systems, content syndication, disaggregation of content & service, disintegration of  content and presentation, content as open code, network as infrastructure…) intermixed with discussions of the democratic, open, diverse, autonomous, unlimited by the capacity of the leader and interactive nature of networks (vs groups) intermixed again with neuroscientific linkages.


Why this makes me uncomfortable is that (amongst other things):

a)      it feels very futuristic (almost as if I can feel a device connected to the network of knowledge coupled with my brain) and therefore not tangible, not for today;

b)      the attempt to measure and manipulate learning effectiveness by measuring the parameters of networks using social network analysis (and thereby perhaps the urge to tweak parameter or two to achieve higher effectiveness) sounds extremely deterministic;

c)      who is responsible for creating the technology, distributing it, maintaining it and advancing it;

d)      who lays down and enforces the rules necessary to make sure cascades don’t happen, for example


Where I think the strengths of Connectivism lie are in the ambition to change the status quo, leverage the power of technology and harness the important developments in diverse but related disciplines such as chaos theory, complexity, self organization and neurosciences.


Let us assume for a moment that all I really know in the world is the following:

  • The set of integers
  • The operator +
  • The results of addition of two identical numbers, 2 with 2 any number of times

Or, 2+2=4. Now I can use this knowledge to recognize and solve certain similar equations:

  • 2+4=2+2+2=6
  • 4+4=2+2+2+2=8

By extension, I can solve other sums such as:

  • 8+4=4+4+4=12
  • 8+8=4+4+4+4=16

Very simplistically, I can replicate a pattern (2+2=4), derive other patterns (4+4=8) in an ever evolutionary manner.

What happens if I now posit that I learn how to multiply identical numbers:

  • 2X2=4
  • 2X2X2=4X2=8

But hold on, I see a similarity or pattern here because I could get the numbers that result from the above two products by summing the first number by the number of times the value of the second number. So if 2X8 is 16, I could arrive at 16 if added 2 to itself 8 times…or if I added 4 to itself 4 times. So that probably meant that 4X4 is also 16.

In the first instance, I was generalizing (and it worked in this instance), but the second one required me to step out, gain the knowledge of multiplication of 2 identical numbers with themselves, experiment and draw similarities or differences (2X4 is not the same as 2+4) based on outputs.

What happened here are three things:

  • I started from a fact represented symbolically using a language and notation I accepted and learnt through experimentation and generalization
  • Then I learnt another fact that was similarly represented
  • Once I learnt both, I discovered that there was another similarity to be found linking the two operations

The facts could have been learnt from a book or a conversation on the web or in-person with my teacher or in of multiple ways. Or they could have been patterns I saw elsewhere and provided my own symbolism for. The cognitive construct that I formed or learnt at a symbolic level served gave me the intelligence to compare and find a third pattern. In this example it was numbers and math operators, it could easily have been any other situation. I don’t even need to be aware of axioms and principles and any formal knowledge to symbolise and evolve these patterns. However, they may be necessary to formalize as I evolve.

Let’s call these patterns or collections thereof knowledge and the capability to compare and evolve, intelligence. When these patterns are collected in a cohesive collection, they may acquire a unique symbol or may get enshrined in a tool or process that may signify that collection of knowledge or of complexity. Learning could then be defined as the process of acquiring and relating patterns, that of demonstrating intelligence, that of actionable knowledge.

Some of my patterns could stop evolving and some could evolve extremely fast. Some could get discarded. Some patterns could conflict with others and some fit exquisitely with other patterns. Some of my patterns could get augmented, refined or discarded as I receive or solicit new patterns through social interaction or experience or introspection. Some patterns could potentially be useful, but not immediately so and would be reserved for future reference. Some patterns could be very sensitive to initial conditions (that 10 to the power -10 change) as in Chaos Theory. Self organization is mentioned by George as another process where the way these collections are organized may itself need to change when new inputs come in or the environment changes.

So far so good. The process of learning so described is what social constructivism also appears to describe. However George mentions that key limitations of behaviourism, cognitivism and constructivism is that they do not “address learning that occurs outside of people (i.e. learning that is stored and manipulated by technology)…(and) how learning happens within organizations”. For the latter point, perhaps there may be disagreement. We need only to look at Communities of Practice to see how this could potentially occur. For the former, well, we use those patterns in our everyday life (e.g traffic lights?) as an environmentally present source that forms part of negotiating knowledge.

Connectivism makes the process of forming connections to acquire and relate patterns in pursuit of learning an explicit focus, while social constructivism makes the negotiation of knowledge in a social and cultural context as the explicit focus. George demands this as an entirely new approach because of a significant change in underlying technologies.

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