Professional Learning Communities versus Personal Learning Networks

Interesting post here on Professional Learning Communities versus Personal Learning Networks by Lorraine.

Choice and options are important in networked learning as shared in my post

There are differences between Professional Learning Communities and Personal Learning Networks. Professional Learning Communities are more aligned with the FORMAL COMMUNITY OF PRACTICE, and there may be mandates as to how it would be sponsored, organised, and coordinated, with definite role definitions for community managers (principal, head teachers, counselors etc.) and other community members.  Those are rules based COP with definite outcomes, and sometimes could be running under a committee structure.

The PLN are more aligned with the Social Network approach where learning is emergent and thus would allow for more personal autonomy.  Previous researches (from our CCK researches) have revealed those observations by Timothy and many other networkers, in their various manifestations of blog postings and forum discussions.

These tensions always relate back to the choice, power and decisions, often associated with communities and networks.  The group versus networks discussion throughout the CCKs would be relevant here.

What is that Theory of Everything?

In this video, Dr. Derek Cabrera proposes that everything is based on these patterns:

1. Distinctions

2. Systems

3. Relationships

4. Perspectives

I think this also relates to the networked society

and Connecting: Interaction Design

#CFHE12 #Oped12 MOOC Emerging as Landscape of Change and Learning Platform Part 6- A Network Ecology

Stephen Downes relates to this post on MOOCs as networks.

Stephen says:

What gets me is that he apparently has *no idea* that we’ve been doing MOOCs as networks for 4 years, and indeed that MOOCs *originated* as networks

Hi Stephen Downes

You have mentioned it in 2007 as I had cited in my post here  in that MOOCs as networks, and YES, indeed that MOOCs originated as networks, and “we” continued as networks with networked learning.

What surprised me is many educators are still looking for MOOC as a “group” and “team” with a shared and agreed common goal, which is where xMOOCs are situated and appropriated. Ideally, a team approach towards learning would likely achieve the vision and mission as set forth, based on the strategies set, and strategic actions development and action.

What might be overlooked in such way of group learning is they are addressing the simple and complicated scenarios of learning, and not the complex and chaos scenarios of learning (and education). I would like to attribute you in pointing out those important properties of networks (in MOOCs), and George Siemens for experimenting with the MOOCs that helps in revealing and validating those principles of network, and Dave Snowden on the framework on Complexity.

I think it may take years, or even decades before x MOOC educators and researchers would fully appreciate that group based learning will scale to a certain level, and that when it comes to huge networks, complexity and chaos principles must be applied in order to make it work. I don’t have all the empirical data to support my argument, as I haven’t got those data from x MOOCs.

However, the evidence collected from cMOOCs supported the networking approach towards learning, and group approach towards learning may be more appropriate under an institutional based, constrained and standardized curriculum, like the current xMOOCs.

The challenges with such xMOOCs are many.

How would an institution communicate its vision, mission and goals and strategies across participants of xMOOC – with hundreds of thousands of them? That is simply not possible. The participants would join xMOOCs as “networks”, form into groups or clusters (some may do it themselves, whilst others may be organised by the professors, TA, or other peers in the areas etc.). Such groups may then continue if there is sufficient bonding among them. Then it would be network-group-network etc. cycle, especially if group members have completed their assignments or discussion. Such pattern would be repeated which would likely be similar to that of learning post CCK08, provided that there are some enthusiastic learners and educators who would continue in practising their Network “leadership”. Is that the fate of xMOOCers and x MOOCs?

Finally, I have also shared in my previous post that such network approach towards learning with MOOCs would develop into a Community-Network-Cluster ecology, where participants would engage and morph along multiple networks, communities and clusters of networks or groups (within and outside institutions).

The c MOOC as knowledge ecologies

Thanks to Stephen Downes for the reference to Dr. Mohamed Amine Chatti’s Knowledge Management: A Personal Knowledge Network Perspective.

Here are some abstracts that I would like to quote:

Knowledge ecologies are thus self-controlled and self-contained entities.

Knowledge ecologies lacked a shared repertoire and are thus open and distributed knowledge domains.

The result of participation in a knowledge ecology is a restructuring of one’s PKN, a reframing of one’s theories-in-use and an extension of one’s external network with new tacit and explicit knowledge nodes; i.e. people and information (external level)

Knowledge ecology is a more general concept than intensional networks.

In essence, a knowledge ecology is a complex adaptive system that emerges from the bottom-up connection of PKNs.

That is a wonderful analysis of knowledge ecology, with a model of Knowledge Management based on Personal Knowledge Network perspective.

I have once conceived that c MOOCs did exhibit the features of community and community of practice, though it certainly differed from the main features of COPs as postulated by Etienne Wenger.

I reckon this knowledge ecology concept re-opens the discourse about the nature of MOOC, in where it functions and operates, and how it behaves, as a knowledge ecology at times.  However, I have often noticed that MOOCs would exhibit the configuration of knowledge ecology – with networks and communities embedded in it post MOOCs.

Here I have elaborated such a configuration in my previous post:

Based on my past experiences with CCKs, PLENK2010 and other MOOCs, the community is quite different from the “typical” communities that we would define, as there is no distinct boundary for the community.  Instead of a community, in MOOC, it consists of numerous networks and communities which formed and re-formed, with some sustained, and some re-configuration in the network-community that formed.  MOOCkers might have morphed along conglomerate networks, or social media as the weeks progressed, thus staying on with a particular media for sometime, and/or created blogs for a particular purpose, and then, engaged with others for a while.  This seems to behave in a self-organised manner, without any directions from any facilitators, but then the individuals within particular networks would set their own agenda, goals, or tasks which suited their needs.

Can one reveal the patterns out of these network/community formation and development?  Some social network analysis did reveal the trend and pattern.

How about this network and community of practice? COPs need a lot of nurturing before they could grow, develop and sustain.

In this article by Wenger and Snyder suggest that: To get communities going – and to sustain them over time – managers should:

*Identify Potential Communities of Practice.

*Provide the Infrastructure that will support such communities of practice.

*Use non traditional methods to assess the value of these communities of practice.

In MOOC, who will be the manager managing the COPs?  May be, there is no one manager, but each of the participants in the MOOC would take up such role, and self-organise the COPs/Networks in a way that suits him or her.

Twitter is a network, though not a community, as many would argue.  But under the “infra-structure” of MOOC, would Twitter be re-defined differently? Is it a transitional community, or communities of practice?  May be.

Photo: Google

Postscript: Here is my post on knowledge and learning ecology.

Finally, I would reiterate about future of education based on a new paradigm of knowledge:

I conceive new and emerging knowledge would be created through such “Global Community and Networks” which would be based on an environment, education and learning ecology with a network of learning platforms such as MOOCs (Massive Open Online Courses), MOOCs (Massive Open Online Communities) and MOOP (Massive Open Online Projects) over different spaces, network chains.

#Change11 #CCK12 Moving beyond Management and Leadership Part 2

What is the difference between management and leadership?

Management versus Leadership is well explained here.

In times of change, transformational leadership seems to provide a superior solution in leading the group or team.  Transformational leaders seek to transform.  Transformational leadership could also be used in peer mentoring.

Another form of leadership is distributed leadership.  It involves forming small teams with distributed leadership.

In the case of networks, what would leadership look like?

In this Applying Design Thinking and Complexity Theory in Agile Organization by Jean Tabaka, the focus of leadership in networks would be based on emergence and resilience.  To this end, I reckon distributed cognition, with distributed and emergent leadership would be a way to go.  This leadership characteristics may be based on the Cynefin Model as developed by Dave Snowden.

Picture: Google image

The sort of leadership style that likely makes sense in networking would then be based on an emergent practice.  This requires an emergent and resilient leadership style to steer the networks.  Action by leaders in such networks include probe, sense and respond in complex networks.

How to move beyond management and leadership in networks?

The most effective sort of leadership in networks may emerge out of a blend of peer leadership and servant leadership.

#Change11 Short Notes on Designing Cyborgs by Jon Dron

An excellent presentation by Jon Dron here. Ailsa has summarised her notes with reflection here.  Jenny has posted here, and here too.

Some notes that I have taken, together with my reflection.

Jon explained about the 3 types of collectives:

1. Direct

2. Mediated

3. Stigmergic – sign-based, sematectonic

Effective collectives:

1. Adaptability

2. Stigmergy

3. Evolvability

4. Parcellation

5. Trust

6. Sociability

7. Constraint

8. Context

9. Connectivity

10. Scale

Jon used the following definition of Technology: The orchestration of phenomena for some use – by Brian Arthur.

I found this similar to the concept of technology affordance.  Whilst Ailsa referred to ANT as a way to describe the relationship between technology and human (the actors in the networks).

I found it interesting when Jon referred prayers as “part of technology” that are aimed to achieve goals, in case of religion, to ask for forgiveness or favours 🙂

Jon sums up that all technologies are assemblies.

Soft technologies – Active orchestration of phenomena by people.

Hard technologies – Orchestration of phenomena embedded in the technology.  This could relate to set of processes, or procedures, which impose constraints on the processes, so steps and instructions must be followed.

Hard is easy, is efficient.  Hard is complete, is brittle, but could limit change and creativity.  If the process is automated, people have little control over that process.

Soft is hard, is incomplete.  This could be part human, part machine.  Soft is flexible.  It enables creativity.  People could have control over how things are used.

Design patterns:


– Adapt

– Aggregate

– Recommend

– Extend


– Automate

– Replace

– Filter

– Limit

Artificial apes – Our technologies are not just reflections of us or things that we use.  They are, in part or whole, made of us.  This sounds like technology in us, and us in technology, and that technology shapes us as good as we have shaped technology.

Good cyborg/bad cyborg

Humans are part of technologies and humans are in control – Good cyborg

Humans are part of technologies and technologies are in control – Bad cyborg.

Operating manuals, legal systems could be one where technologies are in control – bad cyborg.

Some danger signs that a technology is too soft – repetition of boring tasks, the need for skill, complexity and puzzlement.

The holy grail – not too hard, not too soft, just right.

Assembly – Remix, Reuse and Resample.

The use of hashtag in Twitter has been hardened as a technology (i.e. Twitter as a Soft Technology)

What we need would be designing technology – half human, half machine that is just right.

Pictures: Google images