The following readings might help?

Data entry: towards the critical study of digital data and education, Neil Selywn

The politics of transparency and the calibration of knowledge in the digital age, Hans Krause Hansen

On the format, I like the idea of video – if you wanted to extend on this to say more about the theory of your work, or to indicate references and citations, you could write a short textual ‘rationale’ which might give you more scope to be experimental in the video itself.

I’m not quite clear how the manifesto element is going to brought in here (would it relate to the form of the piece in some way?) so would like to hear more about this at some point.

Great to see the thoughts coming together!

You’ve focussed on the statistical measures here, and I wondered if you could say a little more about what you think the implications of this might be. I know that you’ve stated which kinds of things you are comfortable with, but I’m wondering more about the general premise that we compare ourselves to ‘average use’. What does that really mean for a service like Twitter? How has this organisation decided on those particular averages, and what understandings of social media are driving that? It seems to me that ‘tweeting 2 times a week’ is more about a kind of corporate social media strategy, than considering the educative value of reading and/or posting to Twitter. So the broader question here is, who and what are constructing the ‘normal’ Twitter user, and what might that mean for educational use of the service?

Your focus on the stats is also interesting, as one of the core offerings of QuillConnect is the data-to-text computation. This is a burgeoning area of algorithmic web development, where articles are ‘written’ automatically from lots of different kinds of web data. (I was at a data-to-text event earlier this week in Edinburgh http://www.macs.hw.ac.uk/InteractionLab/d2t/). Ed shared this Tweet recently which is typical of this data-to-text field, an article ‘written’ from stock market data:

Here are articles written by algorithms — amazing: http://t.co/oPaTaDSyz7 #mscedc

— Ed Guzman (@emberday) March 10, 2015

Other big developments include sports articles written from various sources of data like sports stats and social media comments.

So, what are the issues around writing text about your Twitter activity? Does that make it more believable? Some of this work is motivated by the idea that stats are simply to ‘dry’, and that people prefer written accounts of activity.

It’d be worth checking over the comments in your WordPress dashboard PJ, and marking the ones that aren’t legitimate as spam – I think once you’ve approved a comment from a spammer, they can then post without moderation, so you might want to just check this out.