Lifestream/Blog Summary

The lifestream/blog has been a challenge for me to create and looking over the past 12 weeks of posts and information it’s given me an overview of what I have achieved and done on the course. The structure is very similar to a learner portfolio and would most definitely be an option for my learners to do in future courses. Creating the blog and working on it, adding relevant links, information, and material has been both engaging and novel. Towards the end, due to work engagements and other issues I wasn’t able to keep up the initial activity I started with and somewhat petered out. Over the course of the first block I had the time to really get involved, collecting images via Pinterest, posting links to relevant films and discussing items with my peers on the course. I would have liked to pull these conversations into the stream, and looking back over the blog it is something I missed out on.

I think the best way to look at the experience of the lifestream/blog is to break it down into its three components and share some of the ideas/feelings I had over the twelve weeks.

Block One: Cyberculture
This was the most engaging block for me. I have a background in film studies and looking at the way Cyberculture is treated in society today was enthralling. The film festival was great and chatting about the films as we watched them was something totally new for me. Linking the opinion of Cyberculture and education; how some aspects may be stigmatised and difficult to accept in society was also something I had not contemplated before the EDC course. There tended to be a trend of fear/positivity towards all things cyber in society at the moment. Creating the artefact and putting all the images together was also a labour of love.

Block Two: Community Culture

The mini-netnography could have been an entire course in itself, it was difficult not to get lost out there! Looking at course content, social interactions, knowledge formation, and course delivery would have made this impossible. The idea of focusing on different users in different languages will most likely be a theme I will come back to in the future in order to research further. I found it was very difficult to stay a lurker and not be involved, but later realised that I may have affected the outcome of the study by guiding conversations and being a direct participant. Ultimately, looking back of the netnography of my peers I wish I could have afforded more time to presenting the information and findings. My peers did an incredible job in presenting their data, and their work made for really interesting reading.

Block Three: Algorithmic Culture

This section on algorithmic culture was awe-inspiring. From the algorithmic play, to looking at the tweetorial results through tools on the net. Deciding who controls the algorithm and the important part in the formation of culture are two themes that I enjoyed the most and I am sure I will come back and focus on them later on.

In summary, the EDC course has an incredible selection of readings/materials combined with highly relevant tasks/activities, it is also very self-reflexive and pushes the learner to contemplate further areas of study related to each block. Many thanks to my peers and tutors on the course, I look forward to sharing the final assignment here.

 

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Tweetorial

Unfortunately I missed the tweetorial, however, as a non-participant, this gives me the chance to examine the findings and statistics from the outside. What can I learn from the statistics and graphs? Firstly, the tweetorial seems male-dominated. I doubt this is the case, but the first graphic places male participants at 90%. The focal point of the tweets came from the UK and North America, with the larger concentration coming from the UK. Looking at tweet archivist, the key discussion focused on algorithms, as set out by the set of questions on the EDC site.

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Given the questions from the EDC site these fit nicely into the themes/questions asked above.

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The mined data links to the questions, however what do we learn from the data? Can we say that the session was successful in it’s aims or outcomes? We really don’t get an idea simply from the summary, we would have to take a deeper look at the actual conversations. We can at least measure whether or not the participants were ‘on topic’ and not focussing on something that wasn’t an order of the day.

What about grading? When it comes to most active users should we reward them? Should the most active users based on frequency of contribution?

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The information cultivated is certainly interesting, yet I feel that we need to look at this information in a more detailed and methodical way. We can get the brush strokes and overview of what went on through the algorithmic data, however in order to have a more detailed picture we should go to the raw data. These tools would be extremely useful in large number MOOCs with 1000+ users where tracking specific themes/topics would be too huge a task to take on.

Week 10 Summary

It has been a little bit difficult, due to work commitments and the busy period just before Easter, to maintain the posts to the lifestream/blog. I have been looking at topics and themes for the final assignment and will most likely focus on algorithmic culture and the art created by Emilio Chapela. Chapela has an exhibition at the Museo de Arte Carillo Gil here in Mexico City, and I would like to investigate themes and links to the EDC course from his body of work on show.

http://www.emiliochapela.com/

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Week 9 Summary

facebook_conn_image_976x462Facebook map of the world from http://www.bbc.co.uk/news/science-environment-11989723

Looking at the use of algorithms and the danger they pose by placing you in a ‘filter bubble’, has been greatly engaging this week. The idea that there are algorithms in place in all areas of online interaction that are working to build up a profile of us as users is also interesting, there seems to be two tensions here.

One is the consumer profile and making sure that we are exposed to specific adverts that we may be interested in, and the second is, as above, the idea of the ‘filter bubble’, there is a chance we may not be exposed to a particular political point of view, a piece of important news, or new discovery.

Another interesting issue is how can algorithms be used in education? How can we harness the power of the algorithm in order to improve courses/content that appeals to online learners.

Adventures in algorithms

This week I went back to Netflix to see what suggestions were waiting for me. Below was a challenge they threw down… Show us what you like, choose some films, we’ll help you find series and movies that you like.

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I had a look at their options and chose these titles:

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Their suggested films were:

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In the initial section, there were choices of films that I had recently watched and ranked, and other films closely related to the genres of film I had been watching.

What was interesting is that I wasn’t offered a broader range of films. I was seemingly typecast :-) into liking rom-coms, superhero based action films, and documentaries on drug traffickers. I didn’t have a mixture of different genres to choose from. My choices, right from the beginning, were limited to recent viewing folly.

I tried to cheat the system. I had a look at the options that were being presented and choose children’s cinema and the genre of horror. My selection looked like this:

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Netflix suggested these titles:

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Who knows where they got drop dead diva from… Possibly my wife’s viewing habits…

Screen Shot 2015-01-22 at 3.13.10 PMI guess the point here is that I have been placed in a filter bubble, the suggestions being based on my recent viewing habits. Harmless here, but what if it was a political view being reinforced? What if it was a perspective on a current affair or report on a certain issue I may not have heard about.

Applying this in Education

Specialization is a wonderful thing. The deeper investigation into one area of study is something that is usually undertaken at Master’s or Doctorate level. I fear that allowing learners to choose their own bespoke route of studies, informed by algorithms, could lead to early specialization in a certain area and lose that rich, transversal, cross-curricular blend that multiple discipline studies create. Allowing algorithms to suggest or guide our educational journey could create similar outcomes in our learners, one that does not perhaps support diversity.

A great tool for looking at your #Twitter #activity in story form. #MSCEDC http://t.co/NiGOwcpMNd

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This report was interesting as I had never seen my activity on Twitter as described above. To be honest, it didn’t highlight anything I didn’t know already (apart from perhaps my interest in politics). This Twitter account was originally created to tweet homework to my first grade students and their parents. I have another private Twitter account and I hope to compare the QuillConnect report from that one with this one over the following week.

 

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