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Our Tweetorial

Our Tweetorial

NB: this analysis focuses on Friday’s Tweetorial activity. The Tweet Archivist search terms don’t allow for a date range to be entered, so this post focuses on only one of the two days in which the #MSCEDC students and tutors were engaged in the Analytics Tweetorial. 

How has the Twitter archive represented our Tweetorial?

Necessarily, perhaps, the archive represents our Tweetorial in quantifiable terms and via a range of graphs, lists, charts and visualisations.

As Colin and Nigel both highlighted in our tutorial this morning, the archive uses rankings. The number of contributions made determines who made it to the top of the ‘Users’ list:

The URLs mentioned and ‘Influencer Index’ are both represented as graphs:

The most used words, user mentions, and hashtags used are represented as word clouds:

The tweet source is shown as a pie chart:

And, finally, images are presented in their entirety:

What do these visualisations, summaries and snapshots say about what happened during our Tweetorial, and do they accurately represent the ways you perceived the Tweetorial to unfold, as well as your own contributions?

These visualisations, summaries and snapshots present us with quantifiable data about the Tweetorial. They are based on counts. From my subjective position, the two most interesting ‘pictures’ are the word cloud showing our ‘top words’ and the image of the Eynon quote. With regard to the former, this does, at least, provide some sense of what we discussed, including Nigel’s ‘cheese bomb’ which distracted us from our focus on analytics. With regard to the latter, this is the only piece of data which does, I would suggest, provide some sort of insight into the depth and quality of the discussion, which hints at a sense of the complexity of some of the ideas which were unfolding (even within the limiting constraints of 140 characters. As to how well the Tweet Archivist data represents my perceptions of the experience as a participant and a learner, it simply doesn’t. Many of my contributions to the Tweetorial were focused on the learner voice, on ensuring that the learner was not ‘done to’ by LA. And, ironically, these representations serve to obscure the learner and the learner’s experience.

As Knox proposes, many practitioners, researchers and big data developers claim that Learning Analytics ‘‘makes visible the invisible’. In other words, there is stuff going on in education that is not immediately perceptible to us, largely due to scale, distribution and duration, and Learning Analytics provides the means to ‘see’ this world.’ I would suggest that this presentation of the #mscedc discussion does the obverse: the qualitative is hidden behind crude quantitative representations. The complexity of the discussion, the pace of interactions, the quality of contributions and, ultimately, insights into what we actually learned from the exercise are missing from these visualisations and lists. They provide no sense of what it is to be a learner and they provide no insights into my experience as a learner within the session.

What might be the educational value or limitations of these kinds of visualisations and summaries, and how do they relate to the ‘learning’ that might have taken place during the ‘Tweetorial’?

However, Knox goes on to suggest that ‘to critique Learning Analytics simply on the grounds that it makes certain worlds visible while hiding others remains within a representational logic that diverts attention from the contingent relations involved in the process of analysis itself.’ What is important is to recognise that these visual abstractions are not reality and that they don’t provide transparent insights into learning; and transparency itself should not be the aim either. Knox again: ‘if we strive for Learning Analytics to be transparent, to depict with precise fidelity the real behaviours of our students, then we are working to hide the processes inherent to analysis itself.’ To focus on how accurately LA represents reality is to miss a sociomaterial trick: ‘my argument is that if we focus exclusively on whether the educational reality depicted by analysis is truthful or not, we seem to remain locked-in to the idea that a ‘good’ Learning Analytics is a transparent one.’ What is key, he posits, is to focus on ‘the processes that have gone into the analysis itself.’ So, in terms of what is presented to us here, for example, the number of contributions is a measure of who is a ‘Top User’. As Knox highlights in his critique of the ‘Course Signals’ traffic lights system used at Purdue University, it’s interesting to consider why the number of contributions is an indicator of being ‘top’. It doesn’t provide a sense of how meaningful or relevant the participants’ contributions were, nor does it indicate other subtle factors, such as whether the participant was engaged within conversations, moving ideas along, or was simply ‘firing out’ their own tweets without reflecting on or engaging with, others’ tweets. The considerations about what factors (technical, social, political, & etc) contribute to this indicator being used is, Knox posits, of real interest. We touched on this in the Tweetorial itself:

What is interesting to consider is how this first experience of the Tweetorial and the associated presentation of the analytics might affect/influence our future behaviours as learners if we were presented with a similar task. As Colin noted in our tutorial on Friday, learners can try to beat the machine and this can have an adverse effect on both learning and outcomes. As a participant, I was aware that our conversation was going to be subject to analysis but I didn’t know what the form of that analysis would be and what the ‘success criteria’ were. Now that I’ve seen them, and if I was being judged on these alone, I would be inclined to fire out as many tweets as possible (regardless of the content) and try and get more followers (to improve my ‘influencer’ ranking). Neither of these would have a positive or meaningful impact on my learning.

Anyone know how to alter the date range here to reflect only our activity during last Thur/Fri? https://t.co/5K1UV9oQPD #mscedc

Anyone know how to alter the date range here to reflect only our activity during last Thur/Fri? https://t.co/5K1UV9oQPD #mscedc

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Comment on Lifestream summary: week 9 by hwalker

Comment on Lifestream summary: week 9 by hwalker

Hi James,

Great to chat with you and the rest of the group in the Hangout earlier.

‘it was great to see how Twitter enabled these other voices to momentarily ‘join our class’.’
Yes: absolutely! It was really exciting to see experts join us. For me, our hashtag functions to offer a sense of us being ‘removed’ from the wider twitterverse; the activity in the tweetorial reminded me that we are, as ever on this course, learning in public.

‘The sociomaterialist perspective of the ‘the constitutive entanglement of the social and the material’ (Orlikowski, 2007) and, therefore, the technical, is a seam which has run throughout our blocks of study and was highlighted in both the Siemens and Williamson readings.’
Like you, I feel this has been a thread that has run through the course, although it has particularly come to the fore around cybercultures and now algorithmic cultures. I think it very helpfully challenges us to move beyond critiques of digital education where we are ‘done unto’ by technology, or simply use technology as the means of production.’
I think this will be – necessarily – one of the focuses of my final assignment. I’m finalising ideas for content and form and look forward to discussing these with you next week.

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Tutorial notes

Tutorial notes

It was good to catch up with James and some of my peers in the Hangout on Friday. Colin made the most impressive entrance: he’d managed to get a green screen working behind him and, as the tutorial progressed, the images (all related to the course) shifted and changed.

The key focus of our discussion was our experience of the Tweetorial, how we felt about it as a learning experience and how our thoughts and behaviours were affected by the knowledge that our contributions were going to be analysed: we were in broad agreement that this knowledge did have an impact on how we approached the Tweetorial questions. However, having seen the fairly superficial data which emerged from the activity, it’s interesting to consider how our engagement might have altered had we seen an example of the sort of analytics which would be generated before we started…

One thing which it was interesting to discuss was that although Twitter is, conceptually, an asynchronous communication forum, a number of us felt pressure to contribute as quickly as possible. Trying to make sense of conversations which had branched and extended over a period of hours was, it was observed, difficult. Thus, for many of us, the Tweetorial experience felt either frenetic, as we tried to keep up with the multiple threads of conversation, or discombolutaing, as we joined complex conversations which involved multiple participants.

The brief notes I took during the tutorial can be found here: Tutorial 24.03.17

 

Comment on Lifestream summary: week 9 by jlamb

Comment on Lifestream summary: week 9 by jlamb

Thanks for this weekly summary, Helen – you’ve really nicely managed to weave the content of the Tweetorial discussion around the Siemens article, the Williamson lecture and also Sian Bayne’s ideas around entanglement and also work by Orlikowski on sociomateriality – really nice synthesis drawing on different courses.

By the way, I love the marginalia and other scribbles on the scanned piece of the Bayne reading: there’s something suggestive there of the way that sociomateriality reveals the ‘messiness’ of education.

‘In the second half of the week, we engaged in a two-day ‘tweetorial’ and I found myself communicating with Ben directly about LA.’

Thanks for your input to the Tweetorial – the exercise was really dependent on the contribution of the group and along with other members of the class, it really made for a compelling and captivating exercise. I’m really glad to see that you managed to entice Ben Williamson into our tutorial! Along with a contribution from Ibrar Bhatt on Friday morning (who also does interesting work around sociomateriality and digital literacy practices) it was great to see how Twitter enabled these other voices to momentarily ‘join our class’.

‘The data generated by our discussion were, to an extent, captured and ordered by the algorithm, but the results are simultaneously ‘messy’ and require human agency to make sense of the ‘cheese’ in the data.’

I enjoyed a wry smile at the way this unfolded and will enjoy looking through the data like everyone else to see whether it comes to the fore! Curiously, on the visualisation in your summary my eye was drawn to ‘STUDENTS GOT SPAM’ which might or might not be an accidental critique on the eating habits of the class (I doubt it).

‘The sociomaterialist perspective of the ‘the constitutive entanglement of the social and the material’ (Orlikowski, 2007) and, therefore, the technical, is a seam which has run throughout our blocks of study and was highlighted in both the Siemens and Williamson readings.’

Like you, I feel this has been a thread that has run through the course, although it has particularly come to the fore around cybercultures and now algorithmic cultures. I think it very helpfully challenges us to move beyond critiques of digital education where we are ‘done unto’ by technology, or simply use technology as the means of production. As you acknowledge, it more complicated than that, more entangled.

I’m going to look forward to reading you blog reflecting on the learning analytics from the Tweetorial. And before that of course, I’ll hopefully see you in a google hangout in the coming days, Helen.

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Lifestream summary: week 9

Lifestream summary: week 9

Having taken the learning analytics course last year, at the start of this week I was back in familiar territory, reading Siemens on LA and EDM and watching Ben Williamson’s lecture on the digital university. In the second half of the week, we engaged in a two-day ‘tweetorial’ and I found myself communicating with Ben directly about LA.

The tweetorial was very much a tweetathon and I was fascinated to follow Anne’s link to some emergent analytics around our engagement and communications over the two days. Nigel’s cheesy diversion had an impact on the data which was generated via Twitter.

It will be fascinating to see what further analysis offers up, but this initial insight provided evidence of the conflicting interpretations as to what algorithms can offer us: order and chaos. The data generated by our discussion were, to an extent, captured and ordered by the algorithm, but the results are simultaneously ‘messy’ and require human agency to make sense of  the ‘cheese’ in the data.

For me, thinking summatively about what we’ve focused on over the last 9 weeks, I keep circling back to Bayne’s term, ‘entanglement’ (Bayne, 2015).

The sociomaterialist perspective of the  ‘the constitutive entanglement of the social and the material’ (Orlikowski, 2007) and, therefore, the technical, is a seam which has run throughout our blocks of study and was highlighted in both the Siemens and Williamson readings. As Siemens highlights, learning cannot be reduced to data:

The tension, the interplay, between the technical – the algorithm – and the human, informed much of our discussion during the tweetorial. Discussions circled back to the subjective agency which informs LA – both in terms of data extraction and interpretation – and to the impact of data on the subjects – both teachers and students. Kitchin and Dodge’s definition of algorithms – cited by Williamson – reminds us that data are not objective:

I’m looking forward to drawing more strands of thought together as we progress into week 10.

Orlikowski, W. J. (2007). Sociomaterial Practices: Exploring Technology at Work. Organization Studies (28)9, pp. 1435-1448.