Learning Analytics: A Traveller’s Guide was presented by Simon Welsh, Manager, Adaptive Learning & Teaching Services – Division of Student Learning, Charles Sturt University.
This was a very informative session and forced me to open my mind to the world of analytics. I have had little interest in this subject previously but Simon’s presentation certainly had me thinking about this topic and the associated implications. Knowing that every click you make during your online study is being recorded is somewhat disconcerting. It leads you to ask – have I clicked enough? Who is seeing this data? Who is judging me by my clicks? Of course, this happens in so many aspects of life including the social social media I access daily, but Simon’s presentation unlocked my thinking in a different way.
Simon highlighted the difference between learning and academic analytics:
- Academic Analytics – supporting the management of students, staff and institutions
- Learning Analytics – supporting learning and teaching processes
Long and Siemens (2011) present the difference in the following diagram:
It was interesting to note that the focus of the uni data analytics at the moment seems to be on identifying students who may show signs of being at risk of withdrawal. This is a very noble use of the data but, of course, care must be taken with what, if any, action is taken in response to the data.
With regard to learning analytics, how can the collection of data be harnessed to create better tools and services? Can this lead to improved learning outcomes?
Simon discussed the challenges facing this field including the identification of what drives quality learning and how analytics can be built to capture what is wanted. How can adaptive learning tools be developed to impact learning outcomes?
Long and Siemens (2011) state that basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision making improves organizational output and productivity. Boyd & Crawford (2012) caution that ‘Interpretation is at the center of data analysis. Regardless of the size of a data, it is subject to limitation and bias. Without those biases and limitations being understood and outlined, misinterpretation is the result.’
Boyd, D. & Crawford, K (2012) Critical questions for big data. Information, Communication & Society, 15(5), 662-679, DOI:10.1080/1369118X.2012.678878
Long and Siemens (2011) Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5)