Archive for February, 2008

Additional connectivism resources and discussion

Friday, February 29th, 2008

For some reason, I’m encountering all kinds of resources or instances of discussion on connectivism. A few resources today:
Situating Connectivism
Connectivism – Teaching and Learning
I should mention as well, all of the talks from our online conference last year have been transcribed if anyone is interested (I keep wanting to get them formatted in .pdf file for printing as a book via Lulu…but time is always a factor. Would anyone find that to be of value?):
Situating Connectivism (G. Siemens)
Research Models of Connectivist Learning (Terry Anderson)

Connective Teaching: How the Read/Write Web Challenges Traditional Practice
(Will Richardson)
The Recognition Factor (Stephen Downes)

Balancing Agility and Stability in Higher Education
(Diana Oblinger)
A Challenge to Connectivism (Bill Kerr)
Learning Conceptualized through the Lens of Today’s World (G. Siemens)

Connectivism Positions

Thursday, February 28th, 2008

I’ve been somewhat peripherally following this discussion on connectivism:

A stand for connectivism:
“Contrary to criticisms against this theory, information and knowledge do not only lie in human brains, but in electronic networks that are constantly moving and being shaped.”
A stand against connectivism: “If any part of the theory were relevant it would be the recognition of the potential of networking and connecting, but these are ways of learning, the pedagogy. Otherwise, the theory does not describe how we learn, how we make the connections inside of ourselves nor does it describe what we learn.”
If the discussion was being conducted with blogs or an open discussion forum, it would be a bit easier to provide comments (none seem to be linked from the wiki)…or to provide links to others who have provided extensive commentary on networked learning in general. Perhaps of greatest value with concepts of networked learning is, as I’ve stated previously, that it has evolved through many contributors – developed, if you will, in the same manner many of us have been stating learning occurs.

Collective or Connective Intelligence?

Thursday, February 21st, 2008

Earlier this week, I posted a short blurb on elearnspace about the importance of connective versus collective intelligence. Several others commented on or reacted to the post, including Chris Lott and Stephen Downes. A fair bit of discussion attended each of these postings. The distinctions between collective and connective are important, so I’ll take a stab at summarizing the conversation and concerns expressed so far.
Discussion initially arose from the Horizon 2008 report (.pdf), which explores future trends in learning and technology. The discussion on collective intelligence (p.23), while important, is a bit frustrating – data, information, and knowledge are used somewhat indiscriminately. Collective intelligence is initially defined as “a term for the knowledge embedded within societies or large groups of individuals”. According to this definition, it is essentially knowledge. A few paragraphs later, it is defined as “knowledge that can be uncovered by combing these open data stores”. Implicit collective intelligence is then introduced as a means to “mine datasets of information from huge numbers of human actions”. The somewhat random use of data, information, knowledge, and intelligence present a challenge in trying to interact with the broader concept of collective intelligence. Is collective intelligence a product of interaction (such as information)? Is it a process (such as creating wikipedia)? Is intelligence a state of knowing? Capacity to comprehend? A property of our minds?
Clearly, if we are going to have a meaningful discussion, we need to come to some sort of agreement on what these terms mean.
Let’s start by providing working definitions of these terms:
Collective intelligence: “is a form of intelligence that emerges from the collaboration and competition of many individuals”. According to this definition, intelligence is not a product such as information or knowledge, but rather a capacity to come function together to achieve a particular task or intention.
I don’t have concerns with the process of collective intelligence as presented here, but I am concerned with the identity-less product which is the consummation of individual work and what is often presented as the work of the collective.
Connective intelligence: individual creation of information, ideas, and concepts which are then shared with others, connected, and re-created and extended based on the interaction.
Simply, collective means blending together. Connective means connecting while retaining the original (though others may build on it in their own spaces).
I’m not arguing against groups or collaboratives. A substantial part of my learning over the last decade has come through interaction and dialogue with others. But the starting point for that learning has not been the collective. The starting point has been based on my own interests and habits. I decided which groups to join, who to read, when to join, and to what degree to be involved. The outcome? I’ve watched very tightly knit groups – such as the three amigos and Twitter sub communities – form and I’ve also seen others (I’ll put myself in this camp) stay a bit more on the outside. But the choice for how to participate rested with each individual because the starting point is a network (or connective) view of how we participate. An analogy that stretches credibility (but only slightly :) ) is the formation of a democracy. Gardner Campbell wisely invoked the federalist papers as an example of how individuals and states create a model of relationship that permits personal freedom and responsibility to the state and its objectives. The tension between Sparta and Athens provides another example. Opinions of an individual’s or a state’s responsibility creates very different societies.
A few other comments:
Why can’t I have both? Does it have to be one way or the other (collective or connective)
Subjects as complex as knowledge and collective and connective intelligence aren’t black and white. While I live in a democratic country, not all of my activities are purely individualistic. I belong to organizations where I accept (non-democratic) direction from others. I participate in groups that requires subjugation of my identity and acceptance of the will of others. However, I am not compelled to join any group. I start with personal freedom to be involved in any group…and to leave that group when I desire. Similarly, in order for me to accept the value of collective intelligence, I must first have assurance that I as an individual, have connective intelligence – the choice to contribute or belong.
Does group membership require an over-writing of individuality?
A few of the terms in the discussion have become somewhat muddied. Individuals are equated with networks and connective intelligence. Groups are equated with collective intelligence. And then we throw in the concept of intelligence. It’s a pretty convoluted mess. Chris Lott mentions this in a comment on elearnspace when he states that the discussion of connective intelligence is largely reflected in his view of collective intelligence. Wittgenstein is ever the pest here. If connective intelligence is a part of Chris’ interpretation of collective intelligence, I don’t think it’s the majority view. Yes, Levy and others provide a clearer conception of the role of individuals in collective intelligence, but common use (wikipedia, Google PageRank, ebay ratings) creates a combined work of the efforts of many, blurring individual roles. The collective is the priority. The product produced by the collective (rating system, recommendations, wiki pages) is the point of value.

Does theory matter?

Alan Levine adds an important voice (and does so, as he states, in the spirit of receiving feedback on the Horizon Report). Most people are not overly concerned with theory. Practical and useful application are factors of importance. If we’re not able to cast the importance of the collective vs. connective (or groups vs. network) discussion in a manner that captures the interest of others who are more practically minded, then we have failed.
However, even if most people are not interested in the theory behind how we organize ourselves online, it is important to highlight that theory has an amplifying effect over time. At a starting point, different organizations of government and society might not appear dramatically different. After a period of time – once the theories are expressed in systems, procedures, obligations, and expectations – a very different image emerges. The discussion of theory is important as a means of anticipating and possibly eliminating future negative effectives.
How does this discussion differ from groups vs. networks? In Stephen’s exploration of this subject, he has focused on tracing the impact on individuals of certain types of organization. I’m seeing the discussion as focused on defining the building blocks for knowledge and intelligence. Put another way, Stephen says the issue is one of control and personal autonomy. I see the issue as one of creating the foundations for functioning in an information abundant world and finding optimal ways to learn and function together. Obviously, the group vs. network and collective vs. connective discussions are related.
Finally, why is this discussion important?
It’s important because of how the outcome influences how we design software, organizational process, and even organizations themselves. Consider Wikipedia – the poster child of collective intelligence. Wikipedia over writes individuality. Yes, yes, I know you can check the history of changes, but the final product is largely a blending of all contributions and if my own browsing habits are an indication, the history tab is not overly used. When books are written collaboratively, the individual is again overwritten (at least in the final product). The collective permits contributions of individuals during the process…but overwrites the individual at the stage of creating the product. What types of examples exist where individuals retain their ideas and concepts? Blogs. YouTube. Podcasts. These approaches produce an outcome that begins with the individual node. What is produced is emergent. Not constrained by the final product of the collective (i.e. the wiki or the collaborative book or the final report). Essentially, as defined by common use (not the definitions provided above), the collective presents a “melting pot” of ideas. The connective represents a “mosaic” of ideas.
Our software and our organizations should be designed in such a manner that permits individuals greatest choice and freedom. We can tie this concept to basic human rights. But a practical component exists as well. Connective intelligence is more lively, more dynamic, more diverse. I don’t read blogs. I read Chris Lott. Stephen Downes. Gardner Campbell. Alan Levine. I read people. Individuals. Collective intelligence suggests I read wiki pages, skim tag clouds, and interact with patterns based on collective activities.

Getting started with connectivism/networked learning…

Friday, February 15th, 2008

During the discussion on ITForum on my paper – Learning and Knowing in Networks: Changing roles for Educators and Designers
(.pdf) – we had a brief discussion on some practical ways to implement connectivism in classroom environments. Here are a few suggestions I threw out (it is a list intended for educators who are just beginning to explore networked technologies, so advanced bloggers/wiki’ers/twitterers will find it to be somewhat basic). Would love to hear how others are using networks to improve quality of learning experiences.
1. Create a class blog…have students blog. Compile their work in an aggregator – such as PageFlakes – that will provide learners with a single page to refer to in order to get an overview of what other learners are blogging about. From my experience, many learners find it stressful simply blogging and are somewhat lost in a highly distributed environment. To build their comfort in these spaces, the use of a central starting spot can be valuable.
2. Use collaborative learning activities – have learners contribute to wikipedia or conduct group work in their own wiki. Better yet, find a colleague at a different university (or school) who is teaching a similar course and create cross-institution collaboration projects.
3. Open your own resources to collaboration and sharing. Start a “english wiki” or “physics wiki” or “psychology 101 wiki” and network with colleagues at other institutions or other countries in developing the resource and keeping it current.
4. To be networked, resources and conversations need a degree of openness. This is one of the drawbacks of an LMS. Learners need to develop comfort with transparency and see the impact. In a recent course on digital literacies, Peter Tittenberger and I found learners can be uncomfortable with posting thoughts in an open public forum. There is something personal (vulnerable?) about learning that certain individuals prefer to keep “secure”. To balance openness and privacy, tools exist, such as ELGG, that allow educators to create mini-networks with greater privacy than the open web.
5. Use existing open education resources in planning and delivering course materials. Focus on using a variety of media – games, videos, podcasts, interviews. Many resources already exist for this type of content…and the list grows daily.
6. Direct students to conference proceedings, recordings, and keynote presentations from recent conferences within the field. Many conferences now record keynote presentations. If the class is focused on a particular theorist or scientist, instead of talking about him/her, direct learners to the source – a recorded keynote or interview.
7. Contribute to the resource pool. When attending conferences, conduct podcast interviews with speakers…or grab a FlipVideo and record the interview…highlight a few key theorists and conduct and email interview and post it on your blog for future class references.
8. Experiment with different tools and instructional approaches. Build a “let’s play” component into your course. Spend a class in Second Life. Create podcasts. Involve learners – have them brainstorm learning activities.
9. Provide learners with resources that will continue to feed their learning after the course is complete. Direct them to blogs, listservs, ning networks, or other communities and networks. The content of a discipline will change. When learners are “plugged in” to a network, they have the opportunity to stay current.
10. Develop learner’s skills in participating in and contributing to networks. Detail meta-skills such as evaluating authenticity of information…encourage them to develop conceptual skills – such as accepting ambiguity and functioning in uncertain environments. Learning projects that focus on building specific cognitive skills can also be wrapped with meta and conceptual skill development components.
11. Combine worlds – involve 4th year (or graduate) students in interacting with 1st year students (in blogs or wikis, for example). Or, as one faculty member has done at U of Manitoba, have 3rd year students write the text book for first year students (
12. Bring in virtual guest speakers through elluminate, skype, or ustream. Reduce the centrality of one educator and shift the role of teaching to a network of external experts and other learners (graduate level, other institutions).
While this is simply a starter list, the key concepts I’m hoping to communicate is the ability to offload content creation, learner interaction, teaching, and skill develop to a network that exists beyond classroom walls. As learners develop their own personal learning networks, they will find tasks such as filtering information abundance, developing meta-skills, staying current, making sense of complex subjects, etc. can be handled through networks. In fact, those tasks can be better handled by networks than they can be with our existing model.
My assertion is that our world is presenting greater complexity to learners than experienced by previous generations. To meet this challenge requires a systemic shift from hierarchies, as evidenced in classroom only models, to networks that take advantage of participatory sense making and emergent curriculum. The educator continues to play a vital role in the process…but her/his role becomes one of assisting learners in creating networks that will enable the development of needed skills and will model the attitudes and skills needed to effectively participate in information abundant environments.

Learning and Knowing in Networks

Monday, February 4th, 2008

I’m currently presenting a paper to ITForum on Learning and Knowing in Networks: Changing Roles for Educators and Designers (.pdf). From the abstract: “Current developments with technology and social software are significantly altering: (a) how learners access information and knowledge, and (b) how learners dialogue with the instructor and each other. Both of these domains (access and interaction) have previously been largely under the control of the teacher or instructor. Classroom walls are increasingly permeable. Google Scholar, Scopus, and open access journals offer increased access to academic resources; an extension to more informal approaches such as regular internet search and Wikipedia. Social software (blogs, wikis, social bookmarking, instant messaging, Skype, Ning) provide opportunities for learners to create, dialogue about, and disseminate information. But what becomes of the teacher? How do the practices of the educator change in networked environments, where information is readily accessible? How do we design learning when learners may adopt multiple paths and approaches to content and curriculum? How can we achieve centralized learning aims in decentralized environments?”
Feedback/reactions/comments are appreciated.