It has been a delightful day here today.
I have found myself thinking about and discussing analytics for much of the day.
I was fortunate to have lunch with two of my PhD students, Dr. Dennis Bryant and Dr. Ron Smith. Each Wednesday we have an unmeeting at the Mizzuna Cafe at the University of Canberra. Ron is a regular, this was Dennis’s first unmeeting with us. Chris Barnes and Mark Gawler were with us too.
Today’s unmeeting discussed Dennis’s research into students’ failing learning journeys which merged with Ron’s research about winning performance in football. The combination of failing and succeeding led to an extended conversation about pedagogy.
Earlier in the day I had posted about Performance Universals in which I was working through some ideas prompted by a paper at the #Ascilite2016 Conference that has been running in Adelaide from Sunday until this afternoon. (My notes as a remote participant following Twitter feeds for three days are here.)
My interest in the conference was twofold: I was keen to learn more about participants thoughts on educational technologies; and to follow conversations about Learning Analytics stimulated by the one day workshop organised by the Australian Learning Analytics Summer Institute.
At some point I would like to explore the connections between the burgeoning field of learning analytics, performance analysis in sport and sport analytics. There is so much to share.
The day was wrapped by following up on a link recommended by Darrell Cobner. He suggested that I look at Nick Clarke’s post Analytics is not just about patterns in big data.
I found time to tweet two quotes the post:
Nick’s post led me to a second post written by him earlier in the year. It has the delightful title Hyenas, lions and city lights – accurately measuring behaviour is rarely straightforward.
In the post, Nick argues for the rehabilitation of the image of the hyena. I thought his points were a great way to end my luxurious day:
Limited seeing leads to unreliable believing, an important lesson for our data-driven future.
The secret is to collect enough of the big picture alongside your targeted measurements, to establish the full context. When I built a data-driven condition monitoring system to combat poor train reliability, it wasn’t enough just to measure data feeds from the suspect components. It needed additional feeds to establish the different operating states of the train, such as accelerating, braking, or coasting, as well as its location on the network. Only then could I have a broad enough picture of the real environment of my subject.
… and to do so with such a delightfully crafted narrative.