At Learning Layers meeting we had the session for theory that guides the project’s technologies and approaches for informal learning at work.
Tobias Ley explained with the figure how the two ways how the concept has been dealt with.
The upper figure relates with the semantic systems that use top-down ontologies for providing support i finding resources for learning. It explains how real world objects or resources in the web are represented internally and represented by the annotations (tags). Such a model for concepts enables creating ontologies that define the relations of objects. The ontologies that guide learning may be considered as archetypical models – some of them are based on common knowledge and serve as community recommendation structures, others’ have become standards and work as top-down obligational constraints to guide with the recommendations the activities with the related objects.
The bottom figure describes the object representations through concept-vectors.
I think this view is ecology-driven. The description of an object – the concept perception through vectors of certain properties (tags) – may be considered as one “organism”, whereas all the object conceptualizations in the (learning) community create the object’s description as a “species” – and this “species” is fit within the niche described by the vector space described by individuals in certain time moment. The most frequent tags create the part of the niche that is community-specific or stabile and may be considered as an archetypical model. The borders for community’s archetypical model within the whole niche are perceptional, and may be related with the frequency of certain “organisms” in time period, as well as the community members’ validation to certain “organisms”. (acknowledged or core members’ concepts may be considered more credible than newcomers’ ones and determine the community archetypical model)
If someone searches help from the concept niche defined by the (learning) community, his own concept definition may be less or more fit to this concept niche and its most frequently used areas (the archetypical model). Adaptive learner may try to accommodate to the archetypical models in the niche – it means following the meaning-patterns defined by many. This learning behavior that may be called pattern appropriation is one of the distributed cognitive behaviors when individual learners orient themselves (or may be automatically guided by) the encultured niches in the meaning ecosystem.
Another interesting thought is, if the concept niche is simultaneously provided to the learner through two niches – as the standard archetypical model and the community-defined archetypical model. The standard archetypical model is usually stabile, while the community-defined model depends of the usage contexts of the concept that are changing dynamically. In this situation the learner’s own concept definition has to “decide” in which niche it is more fit. I think it explains why the community niche of concepts is always having the competitive edge over the standards niche – it requires less cognitive effort to stay using the community-defined archetypical model rather than adopting the personal meaning concepts towards fitting to the niche of the standard archeotypical model.
Yet, if one could see the visualization to what extent both niches overlap, the non-fit (or out of standard niche) parts (tags?) of own concepts could be consciously detected and abandoned in order to adopt own concepts to the arceotypical models.
In some conditions it is actually not useful to stay in the rigid and timely not updated standard niche, but rather to let oneself be guided by the context-tested niche of the community defined archetypical model. This model (and the relevant niche) is less mature in this sense that it may contain areas what are not sufficiently proven to be useful. But it may contain also more useful areas than the standard niche.
In our discussions with Emanuele Bardone we defined two distributed cognitive behaviors – pattern appropriation clearly relates with the niches as pattern-spaces. Every person repeats for himself certain meanings for the concept, and actually therefore creates the personal cognitive niche that is a vector space defined by these trial usages of the concept. The personal cognitive space can be located somewhere within the community and normative niches. It seems cognitively easier to be staying mostly within the range of his own cognitive niche, especially if it is fit to the cultural/standard archetypical niches.
But there is also another behaviour – chance-seeking that is extending one’s cognitive niche with new elements, adapting to be more fit. The chance-seeking can be used for extending the community defined archetypes for the concept. The chance-seeking may be made conscious by visualizing the chance cases that appear outside the current personal cognitive niche. Chance amplification would then mean consciously repeating such cases, testing their viability empirically to extend one’s cognitive niche.
Chance amplification becomes the collective process of distributed cognition when one can see the changes in his cognitive niche in the cognitive landscape of the community (the standard- and community niches of the archetypes) and get recommendations from those community niches. Several persons contribute to these community niches dynamically by sharing their concept-instances (“organisms”) and can be simultaneously aware of others’ chance-seeking events, if these are made explicit. The collectively empowered chance amplification increases the possibility that positive chances would be incorporated to the community niches.
Let’s illustrate all this: There is a standard treatment procedure in medicine that has to be followed for certain disease (standard archetype). As this is tested out by the doctors in actual practice sometimes it works, sometimes it seems not fit to the actual situations. The doctor at every patient’s case creates for himself the description how to do the treatment and this builds up the personal cognitive niche for this disease treatment. The doctor has learned the standards of treatment. Maybe the doctor also discusses the deviation-cases with other doctors. The cognitive niche of this doctor then incorporates and is embedded within the standard treatment niche as well as the community-defined niche. I think for the person these become inseparable. At some moments the doctor becomes aware of that the new case does not fit to his previous cognitive niche and the chances he creates as new treatment must be validated. One way of collective chance validation is seeing if someone else in the community has already tested the similar new treatment, another is making his chance case known to others for validation. In both cases the chance amplification is collectively empowered. And it is more likely that collectively empowered chances will extend the cognitive niches of many persons and thus become common and get incorporated to the arhetypical models of the community niches.
We have just sent away our brand new paper for HCI 2014 conference in Crete this summer.
Promoting distributed cognition at MOOC ecosystems
Kai Pata, Emanuele Bardone
Abstract. The paper proposes describing connectivist MOOCs as a learning ecosystem. We highlight two aspects of distributed cognition epistemic and collective that MOOCs promote and relate these with learning by chance-seeking and learning from ecological enculturation. Finally we outline some design aspects for supporting chance-seeking and learning from an encultured environment in connectivist MOOC ecosystems.
After first year of LearningLayers.eu project (oct 2012-mid sept 2013) i have analyzed what way the project disseminated itself in twitter. It must be noted we never had a strategy, but it has been a self-regulated approach among project members who are actively using twitter.
The main findings:
- We do not have really a commonly understood twitter dissemination strategy
- We reach out only to ourselves and to workmates using project tag and few other institutional tags
- We do not really know, which other hashtags to use besides our project one and nobody uses systematically thematic tags, rather some event tags
- We talk of what we published but we don’t explicitly say what is brilliant or main point in these papers
- we talk where we meet, and what are more notable presentations enabling access to talks
- Sharing relevant documents that project members have found has few re-tweets among ourselves (do these documents matter enough, whats the point behind the paper?)
- in general the tweets around public documents or events (conferences) get relatively many re-tweets, it could be a chance sharing our ideas by adding some context about the project to the re-tweet of the document – up to now sharing documents has not helped noticing LearningLayers and been re-tweeted
- LearningLayers twitter account tweets are seldom re-tweeted even among ourselves – it seems they are just pushing information to project website twitter feed?
Emanuele Bardone shared with me an interesting paper.
Since Emanuele and i have discussed much how the individual chance-seeking events can at some moments become integrated to the cultural patterns, it seems the phylogenetic analysis may enable also to reversely find out when, from where and at what conditions the chances may become part of cultural patterns.
It made me think of analytical technologies. I can easily imagine to describe separate cultural memes and their characteristics (dimensions) and belonging to different (sub)cultures in the excel matrix to run the hierarchical cluster analysis. But how to detect what conditions caused (influenced) the cultural uptake and when the first chance event was, and why it started to cumulate as a cultural meme?
I have summarized my last years ideas into the Ecological Learning Design approach.
This is the paper that i am going to present and test out on my colleagues in Tallinn University where our research is focused on various (digital) learning ecosystems.
The shorter version of it is in the slides