Moving through the meta: Tracing informal learning and literacies through re(mixing) social media analytics

Moving through the meta: Tracing informal learning and literacies through re(mixing) social media analytics
I emailed Hannah with some specific questions. You can read these below. Under this you can find some random thoughts as I viewed. But, you should watch the video and let me know what you think.

So, some questions…and this might be easier to do in a video chat if needed. Most of these are straight from your keynote:
– Definitely agree about metadata. I also wonder about the cookies, and residue left behind as we use these tools. As an example, when we were on the ORCA grant and researching online reading comprehension, I tried to make the case that the browser was learning from us in our repeated testing of instruments with populations. Of course, this is infinitesimally small….but I think it has an impact. And, this was before personalized Google results, or instant search. What do we know (think we know) about how the browser is “filling in the blanks for us” through data, AI, etc as we learn online?
– Love the description of information in learning as permeating, porous, and dynamic. In my own work process, I describe my workflow as being device agnostic and ubiquitous access to my data. Your descriptors are far easier for humans to understand. In addition, you talk about networked field sites, and learning across these spaces. In prior work on MOOCs, we saw this local/global dialectic. Put simple, the importance of having a local node of learners…and the broader global node in the full MOOC. I see parallels in our two models of thinking. But, in all of this, the one thing I cannot figure out, is the role of the “lurker” in these environments. I know that is a loaded term, and there’s multiple ways to frame this important stage of learning…but I was wondering what you thought, in the context of all of the points you made in the keynote.
– Love the designation of research that is natively digital or digitized. I immediately thought about data collection and analysis I’m conducting on the local branch of “Showing up for racial justice.” I have been interviewing participants, and grabbing as much data from their Facebook group as my shoddy work with APIs will allow. But, I’m wondering if I’m still blinded by my attempts to make sense of this as I. digitize my own epistemologies. I’m wondering about hooking up with a comp scientist on my campus to say….”What can you do with APIs?” 🙂 I have about 1000 questions from there…but I’ll stop for now.
– Ummm…the 10th movement? You ran out of time at the end and flipped through a ton of interesting stuff. I was also complaining that I thought you had more time. 🙂

Metadata is data about data. Metadata is a love note to the future.

  • Descriptive metadata
  • Structural metadata
  • Administrative metadata
  • User-generated

How can we use metadata to learn more about our learners when they’re not in the classroom?
Look at the flow of learning and communications in and out of our classrooms.
How does learning permeate through the spaces in and out of the classroom?
What response would Hannah have to the role of lurkers in MOOCs, or other online learning or socializing environments.
Networked field sites: Information and learning in our classrooms is porous (information flows between spaces) and dynamic.
Metamediation…not multitasking.
Application Programming Interface (API)
Research that is natively digital, or digitized. What would this mean as I move my research into an analysis that is natively digital?
we need a legal framework to guarantee at least some access to API data, at least for some people. It is certainly nice that companies start research collaborations, but these fit of course into a sanitized view on their services. We therefore need, I think, something that is able to express the public’s legitimate interest to know “what’s going on” and access to API data is, in my view, a more promising avenue than the forms of purely technical or operational transparency that are often discussed. Fair use principles, for example concerning copyright, exist in academia because there is a belief that research that is not beholden to corporate interest performs a function in public life that is worth protecting. Can we imagine something similar with API data? A legally protected means to do research into these platforms? To find a compromise between privacy and publicness, we would have to find a way to distinguish between “disinterested” research and other applications. 
The public/private continuum – what happens when your data is sold, borrowed, stolen. So, when your data was private, and then it is sold to another group…and becomes public. For example…what happened with Yahoo.
10th movement?

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