Having come across this video by chance, it really spoke to me regarding my themes of connections and student data. During the first section of this interview Gardner Campbell speaks of how personalised learning is when your teacher really knows you. The difficulty being how to scale this ‘thick, rich experience‘ up to hundreds of students.
One possible answer is to ’empower the individuals to scale their own meetings on the network’ and here the meaning of ‘meeting’ is ‘spaces in which humans encounter each other in particularly rich meaningful ways‘.
This desire for connection is something that the web was built for. Yet Gardner asks ‘Is higher ed ready to tap in, in very meaningful, deep ways to students dispositions to connect?‘.
So why the continued resistance? Why the avoidance to teach and integrate communication processes effectively online? Again and again the response to blended or distance learning is to create resources, put them online and let the students work through. The culture mountain is tall and we are still near the bottom as a sector.
I attended this talk at Dublin City University in November 2016 and thinking about the course content nearing the end of our stream lead me to revisit it and Sian’s paper from block 1. The topics covered arose from the Digital Education manifesto and reminded me of how we can look to the future whilst still weaving in the human.
This Tweet caught my eye as it was by someone who is going to delivering a keynote at an institutional event in April and the Tweet prompting the comment is added below. This is relevant for my human thread in that it isn’t the technology that is the worry it is our implementation of it, and whether or not it is for the good of the people.
People misunderstand the danger from AI. It's not that robots will rise up. It's that dangerous algorithms will be made by oblivious people.
Finally, through the analysis of the Tweetorial and discussions during the Hangout I began to suspect that I look at the data perhaps somewhat differently. It was raised that the data privileged quantity over quality and it was clear that in the data sets those who shout loudest and most often win the day.
The data will never be simply numbers to me, I will always want to analyse thinking of the humans as the nodes, joining and connecting. Maybe lots but maybe less, like the organic nature of a tree with many branches and buds, as the ‘visible’, roots and seeds as the ‘invisible‘. I still believe our networks are too complex to be reduced to this binary referred to in Knox (2014).
In order to try to provide some order around the disordered elements of the Tweetorial I used a couple of different methods of visualisation, namely TAGS and Storify. Using these two together with the Tweet Archivist data provided by Jeremy I have answered the three questions below.
A colleague offered to lend me this book knowing about my studies and the colouring book was part of a Medical Education conference pack in February and seemed a perfect element to feed into my stream – I am looking forward to colouring in several of the pictures for my feed.
Hi Jeremy, my annotations were an attempt to critically consider the data that was gathered yet not actually used, as well as what was used. So in answer to your question I think context could be measured to some extent without more sophisticated analytics.
For example, if the report had considered my weekly average with the class weekly average (rather than the displayed course average) then it could have acknowledged ‘something’ going on across the class. So the low numbers would look less stark against the class numbers for each week.
Going a step further by combining this with the data that it was one week before the end of the course it could have provided supportive feedback such as “Congratulations, you are nearing the end of course, keep up the good work. You may be finding it difficult to stay motivated at this point but don’t forget to login/join conversations for the last week – it may help with your assignments(exams).”
Essentially, flipping the existing data into supportive encouragement rather than demotivating ….
from Comments for Clare’s EDC blog http://ift.tt/2nwu9ut
At the beginning of the week I focused on looking around to get some basics about Learning Analytics (LA), adding links to the HEA and Jisc for example. Overall in general I got the sense that the advantages of LA for the learner was for the institution to be able to provide support and guidance. The proof of this seemed to be increased retention. However, I then explored many examples of algorithms gone wrong and the human impact of this.
I have been a LA sceptic without having an in-depth knowledge on the topic and I was quite surprised that during the tutorial many shared my cynicism and highlighted the need for more qualitative and contextualised analysis.
As education has higher and higher student numbers and fewer teachers LA is yet another way to solve this problem – I tried to show this on my image – but I don’t believe that it will. In any conversation I have ever had with others LA is always framed in terms of surveillance. Essentially, ‘we need to track our content to prove student didn’t engage’. Every time I ask why. ‘Proving’ the student has clicked on content is absolutely no proof of engagement. Only a human making contact, face to face or virtual, can deduce this and know if the student is having problems or is simply working at their own pace and timetable. I added my own annotated LARC report to help me frame this.
In addition, there is also the issue of ethics around the collection and analysis of all this ‘big data’ and I tried to highlight this by including the blog post by Lorna Campbell who tried to question a software company on its collection of data policies.
I ended the week by including a Storify and a TAGS Explorer map of the, very informative and enjoyable, Tweetorial as I tried to visualise the conversation as this helps me see past the numbers.
I really enjoy creating these visual representations of social networks – I try to focus on the connections and not the ‘top’ quantitative data. Knowing that the software is open and free for all to use also highlights the community feel to the process and Martin Hawksey, the creator, always welcomes interaction. A true example of online community.
It seemed only fitting to include my LARC report for the week after discussions around learning analytics. I have annotated it briefly with some notes but it definitely serves the purpose of highlighting the need for context. Without any context it would baffle an outsider, showing a poor level of social interaction, engagement or attendance.
A quick overview is that it was week 9 of a ten week course and two assignments were looming in my mind. I was focused on working on my learning challenge for the first of these and prioritised this along with reading and planning my writing. None of this was captured in the ‘numbers’. It also didn’t compare the week with my fellow classmates but to the course average which meant there was no direct comparison. This might have served to show some of the context as many of the others would be generating similar reports for the same week.
This short clip with Bonnie Stewart (referenced earlier in the module) sums up eloquently the importance of networked learning and community in our unstable world. It highlights the blend of human interaction with network communication and how one informs the other.
I think this is the only TV show that I have watched every session in full, right to the end. Looking at where we are now, it seems pretty much on course to be a prediction of our dystopian future – algorithms in control 😱
from Comments for Clare’s EDC blog http://ift.tt/2m8P7jb
I posted this blog post in the Tweetorial as well as embedding it here as it highlights the grave need to call out software providers to consider the ethics around people’s data and stop privileging the surveillance as a selling point. It is yet again a ‘because we can’ function and Lorna Campbell demonstrates that the voice of reason may be a small voice in the dark. This all seems to resonate with the general direction of our discussions this week and the reality of a surveillance society, framed to us a method of keeping us safe.
This short excerpt from the BBC considers large scale data driven algorithms as a parallel with legal systems in that there is no perfect solution and it is a system of smaller parts coming together as one.
Two Microsoft researchers may have blown the lid off a secret, or at least an assumption, that most of us have about artificial intelligence (AI), with serious repercussions for how we think about this emerging technology and its use in everyday life.
The dystopian future portrayed in this television show set in the present, seems ever increasingly nearer. It is a tale of one man’s invention of a machine which ‘thinks’. Ultimately, it came down to man against machine, man against man and machine against machine. The ethics were at the heart with the inventor constantly worried about the impact of his invention on the human race. Despite the fictitious basis it constantly raised timely and important questions that we need to ask ourselves in a technological era.
Just Pinned to #mscedc: http://ift.tt/1xkIKIq | Sensemaking with Learning Analytics @gsiemens #apereo14 keynote | I’m connecting the dots and hoping the apereo community gets on board with a full scale development of learning analytics open platform as an LMS plug-in.