So… Unit 4 with “the Radicals” kicks off with a nice intro to Learning Analytics, something which I’ve been aware of for several years but haven’t explored in depth. Now is a good opportunity to read up on it and its benefits – and drawbacks, as I mentioned in a previous post when summarising the book Is Technology Good for Education? According to the book, (Siemens et al., 2011) describe ‘learning analytics’ broadly as:
the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
All well and good, but I was interested to read that many of the tools and techniques for learning analytics come from the business world – website analytics – that kind of stuff. Imagine you’re a young adult. You make a purchase on Amazon and you watch a Youtube video. The next time you are online the Internet tempts you with similar books and similar videos because its algorithms figured out you might be interested. This is the aim of learning analytics! As a student you work online, taking part in some activities and not others and the same types of tool which tracked your Amazon and Youtube habits ideally will clue into how likely you are to make the grade and what your teacher can do to help.
As I also mentioned in the previous post, Learning analytics is currently high profile in Moodle as we are developing, with the help of the community, a core module which will deliver:
- Description of learning engagement and progress,
- Diagnosis of learning engagement and progress,
- Prediction of learning progress, and
- Prescription (recommendations) for improvement of learning progress.
Here’s an early presentation on it from HQ Researcher Elizabeth:
While this initiative has been generally welcomed, there are some who share the concerns of Selwyn (2016) that in our enthuisasm to automate data collection on students in order to improve learning, we might be both losing the ‘human touch’ and also still being biased in our choice of data.:
The danger exists of educational data systems only measuring what can easily be measured, rather than what cannot easily be measured but is nevertheless important
Biesta (2009) asks the question:
..are we indeed measuring what we value or […] measuring what we can easily measure and thus end up valuing what we [can] measure?
Well – as a newbie to Learning analytics, I don’t have strong views or answers, but I am looking forward to the next two weeks of Unit 4 and how I can contribute.
Selwyn, N. (2016). Is technology good for education?. 1st ed. Great Britain: Politybooks.com.
Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K. and Baker, R. (2011). Open learning analytics: an integrated and modularised platform. Society for Learning Analytics Research.
Biesta, G.J.J. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability 21(1), 33-46. [DOI: 10.1007/s11092-008-9064-9]