I had the pleasure of 'crashing' the White Rose Learning Technologists' Forum yesterday, tempted by the focus on Learning Analytics.
Martin Hawksey led the main session of the day, talking about Learning Analytics: Threats and Opportunities, followed by Patrick Lynch from Hull talking about the work he's been doing. In this post I'll try and explore some of the themes we looked at. Twitter activity used the #wrltf hashtag if you want to take a look at that.
What is Learning Analytics?
Martin talked about it being important to see the links between Learning Analytics and more mature fields and disciplines such as Network Analysis. We looked at a couple of definitions which you can read in his slides. The definitions talked about using data and analysis to understand and optimise learning, and to develop "actionable insights". We took part in discussions where the need to consider the differences between business and learning analytics become clear.
Threats: 'The Absence of Theory', 'Visualisations', and 'Ethics, privacy and data sharing'
Mike Caulfield wrote about how Big Data is usually used in a Behaviouralist way and how it "asks us to see entire classes of people as sets of statistical probabilities". He argued that we need theory to guide us as to what we are looking for.
Caulfield also argues that "it is actually “small data” — data that can live in a single spreadsheet — that paired with local use has the greatest potential". This is an aside in his article, but is probably the main thing that I took from this event.
Martin led us through some other thoughts on why decontextualised data is not useful, before moving on to the dangers of taking dashboards and graphs as neutral things, when really they are almost always designed to tell a story in a certain way.
We then looked at ethics, with InBloom used as an example of an educational initiative that many thought was unnecessarily collecting user data that all sorts of people could then access.
Learning Analytics can help start conversations, and act as a feedback loop between students and instructors. Martin Cooper has done work on how it might be used to help disabled students.
An important quote from a Simon Buckingham Shaw presentation is used "What kind of learners are we trying to create? This should drive our analytics".
Tools we can use
Both Patrick and Martin demonstrated tools that we can use to collect, manage and use data.
Martin talked about the various ways in which Twitter is used by teachers and then demonstrated his TAGS (Twitter Archiving Google Spreadsheet) tool as a way of archiving Twitter activity. When planning to use this it is worth knowing that currently the Twitter API limits Tweets to those posted in the last 7 days, and to the last 18,000 Tweets in a series.
This tool takes 5-10 minutes to set up for yourself, and I used it to archive/collect the tweets related to the event.
Patrick demonstrated the use of Tableau as a tool that he uses along with the 'R' language to explore data, looking for oddities that might reveal an interesting story. For example using Tableau to present data from a course they noticed that lots of students were accessing a resource after the module had finished. They asked questions and found that the students were finding it useful in another module, and the resource was then embedded in that other module too.
For those interested in exploring these things further, Patrick recommends engaging with the Apereo Foundation community and keeping track of how JISC is promoting and investing in Learning Analytics on our behalf.
In his experience it is important to work with students from the beginning. Some students see it as Big Brother and others as something useful. In the future he thinks the collection of data is likely to change from opt out to opt in, and if that is true we will need to ensure that students both see and receive a benefit from it.
Previously my perspective on Learning Analytics was that it was something that would only be useful in online courses where you could collect huge amounts of data about all a student's learning activities. Yesterday's event introduced me to smaller scale ways that we could collect and use data to benefit student learning, and a convincing case was made as to the smaller scale very focused work potentially being more effective.