Learning stats

I've read a lot about the edtech industry implications of MOOCs, and educators' pedagogical concerns regarding their implications, but it's only now (mid-2014!) that I've finally enrolled in one. I've come to the understanding that my exploration of my data in R is hampered not by my knowledge of the tool, but rather by my lack of solid statistical knowledge. It's time to fix that.

I was surprised at the number of stats MOOCs out there - multiple instances of even the same title from multiple universities and multiple MOOC providers. There's a great range of choice. And delightfully, most of them seem to use R as their tool of choice.

I'm going to jump mid-way into a series of courses from Johns Hopkins (https://www.coursera.org/specialization/jhudatascience/1/courses), hoping I'll be fine based on my mathematical background and having had a few months playing with R.

Scale

There has been a lot of interesting discussion online recently about Connectivism and learning theory lately - particularly on Jon Dron's blog and Stephen Downes' responses to them. I'm still very much in two minds about Connectivism as a theory. I'm not sure how it is different to Networked Learning (they seem to be the same practically, if not epistemically), and my inner positivist always screams that "knowledge" is something that resides inside people - that the stuff that resides in these networks is really something very different, even though it is a source for knowledge.

One sentence really stood out for me in Dron's post from last Friday ("What is Connectivism?"):

More is different in a networked system, resulting in a) large scale patterns and emergent behaviours not usually seen in smaller systems, and b) linear benefits of scale - a 1% contribution rate in a network of 10 people will be of a lot less value to all the people in that network than 1% contribution rate in a network of a million people.

It's not something that is a new concept to me - but it made me reflect on my project and how it performs, and has made me think differently other things I've read since then. That 1% contribution rate is not an unreasonable estimate of how likely people are to add ideas into a network if there are no extrinsic motivators; but then the network of ten people most likely has no contributors, and the network of a million has ten thousand. Ten thousand contributors can generate an awful lot of of content, and communities can be formed around that content. My own data bears these numbers out:

I have a group of over a thousand potential contributors - but only 37 (3.3%) have uploaded anything, and only 11 (1%) have contributed five or more resources. There is almost certainly not enough content and activity to generate a significant community around these resources.

I intend to keep improving resource tools in the hope of lowering the barriers to participation to the point where a community can self-start based on these numbers. But this has been a clarion call for me to start thinking about problems of network scale, and what they mean for my project.