Open Analytics?

Digital Pedagogy Lab : Prince Edward Island : Third Reflections

So the reflections keep coming, and the connections get stronger as we continue to reflect in the open. There have been some great blog posts from fellow #DigPed delegates in the last few days and maybe at some point we should write a collective piece – just a thought. This is a reflection about a conversation, a new thing learned and some personal revelations.

While I was in a discussion with Robin DeRosa, Daniel Lynds and Scott Robison we started thinking about open, open education, open educational resources, open data and eventually we started asking about analytics systems that are in use in institutions, either as part of the LMS / VLE or elsewhere (for example library systems, access systems etc.).

And we also took to twitter.

My understanding of analytics is limited. I understand that data is generated by students (and staff) and that there are various tools that can take that data and using a variety of algorithms add some context, thereby either giving the student a representation of what they have done, or predicting what they will be likely to do (in particular: what they are likely to be successful/unsuccessful at). It is worth noting at the moment that some of the analytics systems in place do not allow students to interrogate their own data or the algorithms in place used to make assessments of their performance.

We asked the question:

What would open analytics look like?

There are “open analytics systems”, but underpinning our question was the issue of the data being open to each of the individual students, and the transparency of the algorithms associated with the analytics being used.

So:

If we make the data open to each student, can we give the students the tools and the space to develop the capabilities to understand the data they are presented with?  Could this be a way of framing a conversation with institutional staff about their learning processes?

Could analytics be one way of thinking about assessment in a connectivist learning environment?  Could learning analytics become a part of the suite of methods we have for discussing their progress, and developing their own learning journey to suit their needs and aspirations, and adapting the processes as their journey continues?

Further:

If we can engage students in the understanding of the data that is held about them, is that one way of developing their data and information literacy as one of the key outcomes for their digital capability in a connected world?

Attending #DigPed and specifically Daniel Lynds Data Visualisation Workshop made me think about my own analytics, about the data I generate and how it can be viewed. I used Socioviz  to look at 24 hours of my own tweets.

Network Analysis

Me: 24 Hours on Twitter

1 This red area represents participation in a #DigPed twitter chat

2 This blue area was started through various tweets and retweets about blog posts that myself and other participants in the digital pedagogy lab wrote. Note the limited cross over with the #DigPed twitter chat.

3 In yellow a short exchange with the @socioviz account as I state that I am using it for analysing tweet activity

4 This small blue area was an early morning RT that gained little traction

5 This large blue area is social chat and “fun” in my distinct “Birdwatching” group of followers. The subject was discussing bird tattoos and the feasibility of generating a complete book of British Birds illustrated by tattoos.

6 This pink area represents a small conversation where a friend included me and two others in chat about canal boats.

7 This green area was a short exchange with two colleagues

8 In terms of data literacy, analytics and visualisation this is probably the most interesting grouping. From my perspective the purple area is passive, I did not participate in the conversation (which was centred around a workshop my colleague James was running) but shows how I was included in the conversation through including @Lawrie several times, even though I didn’t participate.

So what does this mean? Why is it important?

Everyone should acquire the skills to understand data, and analytics. Visualisations are used by a range of organisations and the Media to convey information, and not all of it should be taken at face value. For example in the graphic above the purple and red areas could be seen at first glance as being the same in terms of active engagement (in fact the purple looks more active than the red), but only by drilling down to the content do we realise that the red area is active conversations (and learning?) whereas the purple area is passive, and I am mostly absent.

When we start thinking about the next generation of learning environments and the potential of big data in learning, especially the use of analytics we need to remember that the algorithms give us limited perspectives. Context and interpretation is essential, and this may be done by institutional staff, but it is more important that we equip students to create their own understanding, and enable them to navigate their own learning journey.

Finally, look again at the visualisation of my Twitter data. Each of those circles is not just a piece of data, it is a person, interacting through a digital platform, but still a person.

The visualisation may look like data, but it is a snapshot of how I am connected, it is my rhizomatic digital landscape. For me it reinforces the fact that digital is people.

 

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