The dashboard analyses from Social Monitor and Tweet Archivist present the statistics in a nice visual manner. But I am not sure that they tell me anything I didn’t know, however, if I was presenting this information to others then they would be very useful.

The busiest time was Friday 13 March; certainly true for myself as I was able to participate on this day.  The topics under discussion, and the sites linked to, kept very closely to the course themes of algorithms, learning, education and data; this is not surprising as the questions we were primed with revolved around these. The hashtags look slightly different with #codeacts and #learntocode featuring very heavily;  I wonder if this was because most of the conversations did not use hashtags so when one was introduced it quickly made an impact on the statistics.

There were a similar number of original tweets and replies which imply that conversations were brief. However, it is also likely that because we were using Twitter synchronously people were not using the reply button in the conversations; possibly because they take up valuable characters. From my point of view, as a participant, it did feel that there were lengthy conversations with several participants taking place.

@SianBayne features as the most influential tweeter with the most mentions and the most klout.

 

 

Algorithms are everywhere; we depend on them to filter information for us.  There is so much information produced every day on the internet that we need help to sort through it, to find the important information and bring it to our attention.  Every time we install a new app on a device or sign up to an email list we are activating an algorithm and giving it permission to send us information often based on preferences we assign.

When we use an app we also give it permission to collect data on us. Spitz (2012) advises us that mobile phone companies collect data on us, such as phone logs, location based data, contacts etc. all of which could be used to create a profile of our daily lives and movements and networks.  In essence, it is a trade – our data in exchange for filtered information.

Algorithms do more than just filter data. There are learning algorithms (Zarkadakis, 2015) that are teaching the next generation of robot to become smarter. They can be creative – write poetry and stories (Podolny, 2015), or play music and dance (Bretan, 2015).  They are even giving us, humans, cause to make physical changes to the environment so that they can work more efficiently (Slavin, 2011).

But is the field of education keeping up with the rest of society in its use of algorithms?  Or are educators ‘behind the times’? (Selwyn, 2011).  With the relatively recent advent of MOOC style courses we now have a resource of ‘big data’ that can be used to identify and categorise student behaviour (Knox, 2014).

MOOCs are generating vast databases of information for researchers to interrogate, but inevitably, anything new has ethical implications that need to be dealt with.

References

Bretan, M. (2015) “What you say” – A robot and human musical performance. Available from: https://www.youtube.com/watch?v=O-bjTfYILPs [Accessed 15 March 2015].

Gillespie, T. (2012) The Relevance of Algorithms. In: T. Gillespie, P. Boczkowski, & K. Foot (eds.). Media Technologies. [online]. Cambridge, MA: MIT Press. Available from: http://www.tarletongillespie.org/essays/Gillespie – The Relevance of Algorithms.pdf.

Knox, J.K. (2014) Active Algorithms : Sociomaterial Spaces in the E-learning and Digital Cultures. Campus Virtuales. 3 (1), pp. 42–55.

NYTimes.com (2015) Did a Human or a Computer Write This?. Available from: http://www.nytimes.com/interactive/2015/03/08/opinion/sunday/algorithm-human-quiz.html [Accessed 13 March 2015].

Podolny, S. (2015) If an Algorithm Wrote This, How Would You Even Know?. Available from: http://www.nytimes.com/2015/03/08/opinion/sunday/if-an-algorithm-wrote-this-how-would-you-even-know.html [Accessed 13 March 2015].

Selwyn, N. (2011) Does technology inevitably change education? In: Education and technology: key issues and debates. London: Continuum International Pub. Group. pp. pp. 20–39.

Slavin, K. (2011) How algorithms shape our world. Available from: http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world [Accessed 16 March 2015].

Spitz, M. (2012) Your phone company is watching. Available from: http://www.ted.com/talks/malte_spitz_your_phone_company_is_watching [Accessed 13 March 2015].

Zarkadakis, G. (2015) Games Arcade Spurs Robot Evolution. Available from: http://www.huffingtonpost.co.uk/george-zarkadakis/garobot-evolution_b_6837378.html [Accessed 13 March 2015].

My puppy dog Google Instant search results:

   

As expected.  Coincidentally, I came across a list of blacklisted words during another exercise – this list was held by Shutterstock who explain how / why they use a list: “Miscellaneous caveat: Clearly, what goes in these lists is subjective. In our case, the question we use is, “What wouldn’t we want to suggest that people look at?” This of course varies between culture, language, and geographies, so in the end we just have to make our best guess.”  

I also completed the Facebook challenge which did show different results but due to my almost total lack of use/interest in Facebook I was unable to determine why the results differed.

So on to the final challenge – Google Ads. I was under the mistaken impression that I had turned Cookies off, but then I remembered that I had turned them back on to use the Collusion app (a cookie tracking app).  Anyway I had 20 results. Not surprised by the ISPs and Phone Service Providers as I have a very troublesome broadband connection.  But what on earth is Executive Branch?  The Dictionaries entry made me smile as I regularly use the Define or Synonym keyword on Google when I need help

 (Click on the picture for the full list)

My immediate response to this exercise was to opt out of interest based ads on Google.  I really don’t like these; I find it very invasive when these ads appear, I much prefer the random ads.