If you want to delve deeper into IFTTT (sign in with your UUN) https://t.co/9urxCnzwzg#mscedc
— Eli's inane rambling (@Eli_App_D) March 14, 2017
It’s no secret that I’ve fought a long battle with IFTTT.com in order to get it to act the way I want and do the things I expect of it. This week I chose to look at it from outside the setting of my course blog and look at the tool in its “natural” habitat and I was actually a bit impressed. It can do some really useful things to help make life a bit easier, like send a text to my wife to let her know I’ve left work, or send a text when I am at a certain point on the journey home. This one is handy for knowing when to put the tea on but I found another use for it. I thought it would be a great piece of data to use to show that algorithmic data can easily be misinterpreted and how different people might interpret it differently.
I set this up to publish a post to my blog to let the world know every time my phone GPS picked up that I was at Moray House, School of Education. My thinking that as a student of Moray House, this would be seen as significant and could be interpreted that I was there to visit the library of for studies. The fact that the algorithm should kick off twice every day, once in the morning and once at around 5:15 pm, I thought might imply that I as arriving and leaving for my day’s studies.
My intention of this play around with the algorithm was to see what conclusions my classmates drew from the minimal data:
- Eli is a student of Moray House School of Education
- Eli’s GPS from her phone is showing as at Moray House School of Education each morning at the same time and
- each evening at the same time.
It’s not a lot of information to go on and therefore involved “interpreting” what this information means. This was exactly the point I wanted to make, that with learning analytics, we are interpreting data, when we may not actually have enough of the picture to fully understand that data in context. As Yeung( 2017) stated of algorithmic use, in her paper concerning the use of data to affect behaviours,
Big Data ’ s extensive harvesting of personal digital data is troubling, not only due to its implications for privacy, but also due to the particular way in which that data are being utilised to shape individual decision-making…
Unfortunately, my experiment didn’t happen as, yes you guessed it, the IFTTT algorithm didn’t work, not even once. So instead of having a minimal amount of data to interpret to represent our possible failures of learning analytics, we have an algorithm that doesn’t fire at all and returns no data. I guess this gives us a whole different learning experience and another algorithmic potential to be critical of.
References
Yeung, K., 2017. “Hypernudge”: Big Data as a mode of regulation by design. Information, Communication and Society, 20(1), pp.118–136.
Shame this idea didn’t work Eli, it would have been super. Any ideas why it didn’t work – which trigger were you using?
‘It’s not a lot of information to go on and therefore involved “interpreting” what this information means. This was exactly the point I wanted to make, that with learning analytics, we are interpreting data, when we may not actually have enough of the picture to fully understand that data in context.’
Really great critical point -much better than the applet actually working! I think this highlights very well the kind of dilemmas we face with learning analytics: data points can only ever be proxies for learning. However, I expect most data scientists wouldn’t argue against that, rather they’d suggested that one just needs to find a good proxy!
Hey Jeremy,
A bit of a google revealed that there was a known issue with the GPS and IFTTT talking to each other on android platforms, I wondered about switching it to my iPhone for the sake of the experiment but by then we were a few days into the week and I felt that it would have been too late to make anything meaningful out of it. I felt this one needed the repetition to work properly.
I think it would have been a fun example of interpreting data, so it may be something that can be used again, and as you said would have made a fantastic point that we are really only interpreting data based on information we have or have already interpreted and this means it is flawed.
Eli