Gillian, I enjoyed your blog post about designing learning for adults, also my background. I liked the idea of learners collecting their own data based on their learning patterns supplied through learning analytic means. This way they can understand how they work with content, etc, and become cognitive of their learning patterns, thus making decisions for themselves on what and how to engage.
I was struck by a notion given by Marissa Mayer of Google in a video provided in Week 3 of this course. Instead of being influenced by the content that is found through a general search online, our context (lives, desires, ideas, etc) would become the influence of that search and drive it. I can see this extending to learning. When we post an idea, etc. it would be unique to have our context come forward to shape and influence that post, such as images, sound, colour, data, etc. Who I am would come with me when I engage with content and others.
This collective self would change and morph over time presenting who I am at that time. To create a specified ontology ahead of time (as discussed in this thread) would negate that personal development and the emergence of new ideas to share with others. I believe we need to make way for evolution.
And, I don't fear that the unfettered development of ideas would become eccentric and chaotic creating misunderstood content. We are humans after all, and we do have limited processing powers that rests on previous knowledge and experiences. In essence, I don't think we would lose control but instead allow room for the expansion of thoughts and expressions.
"This collective self would change and morph over time presenting who I am at that time. To create a specified ontology ahead of time (as discussed in this thread) would negate that personal development and the emergence of new ideas to share with others. I believe we need to make way for evolution. "
Kelly, this is spot on. There is no one conceptualization of a learner. Its like the Hiesenberg Uncertainty Principle ("the more precisely one property is measured, the less precisely the other can be measured.") can be so aptly applied when we talk of describing the "collective self".
This is why personalization is so very tough. And it is precisely why BIG data is deficient because it makes for aggregate personalization (stereotypes).
It is also where RDF/Linked Data could be potentially most useful if only it could also *model* relationships (impact of changes) instead of just *describing* them. That is where Connectionists feel they can contribute because they do attempt to *model* the relationships.
I still feel uncomfortable about the difference in the way connective knowledge is defined by Stephen and knowledge is defined in the Semantic Web discussions. For the semantic web, knowledge is propositional and symbolic. Connective knowledge is neither (to the best of my understanding).