Category Archives: ALT

Results Filter Bubble Workshop at #ALTC

So, that was a very inspiring trip, especially to get back some focus on my personal research.  The very best part of course, was meeting the many new and interesting people – so thank you so much for the conversations, #altc community! This post is on the results of the workshop “How do you break your Filter Bubble?” on the topic of Filter bubbles and Personal Learning Networks that I held at ALTC.  I’m going to write a more detailed reflection on the conference itself, so look out for that post very soon.
This was a very lively workshop. I know you shouldn’t say that as the organiser, but when I submitted the idea, I mainly wanted to start a debate on the filter bubble (or the term “echo chamber” – as I learnt – is more generally used in the UK) and the problems it poses for learning and personal learning networks. It seems that this topic speaks to many as I had some interesting reflections and conversations on it, before and during the workshop. During the workshop we had 9 participants and decided to split up into 2 groups. Below I have tried to report the results of the discussion on each of the points, and added some of my commentary in italics.
 

Some general notes

I learnt that the filter bubble need not necessarily be something negative, as it provides comfort, and a safe space to articulate your views, without fear of being attacked for them. So there is certainly a need for the filter bubble in some cases. The issues comes when your filter bubble intervenes with your learning or real view on the world.
Because isn’t the nature of learning, that you need to be out of your comfort zone, so that you learn? 
 

On Diversity in a Personal Learning Networks

The determinants of diversity in PLNs was the issue that drew most discussion, as it was deemed by many that diversity is difficult to label.
Some determinants noted were things like : education, class, aggression, funding, ease of access, interests, advertising revenue, cultural perspectives/aspirations and government intrusion into services, and chances of serendipity. The core understanding was that “ideas were different from people.
 
Also, sometimes the effects of filtering can also bring unexpected/non-deliberate diversity in your bubble.  e.g. because you choose to follow people with whom you only share one well-defined interest (for example, a hobby), you might actually get in touch with people who have diverse views from yours.
It was also noted that filter bubbles are not necessarily online phenomena, but also offline/in real life, we have systems that reduce the connections we can make. The items that make up diversity in these two areas are illustrated here.
 

 
I was very glad to read the quite wide range of determinants that were noted down. I would invite the participants to comment further on them and maybe explain them in more detail as a comment to this post. What amused me about the discussion was the fact that this discussion again showed the limitations of our technologies. Although it seemed artificial to pinpoint what could be a determinant of diversity in your network, ultimately technology does exactly that, as it has only uses limited types of measures and data available to take decisions on what to show you, the user.
 

On discovering that you are in a filter bubble

The participants agreed that most often we notice that we are in a filter bubble after getting a shock of some sort. Many shared their experiences on the discovery of 2016’s Brexit vote and the 2016 US Elections, and how “they had not seen it coming”.
 
It is interesting, that we seem to find out we are in a filter bubble when something unexpected happens – that does not fit in with what you think the world looks like. I wonder if we need this kind of ‘shock’ effect, to start re-assessing our understanding, but also to review the limitations of our perspective (and the things that determine our perspectives). This reminds me of concepts of breakdown and rebuilding and negotiating understanding (http://gerrystahl.net/cscl/papers/ch14.pdf)   
 

So what can we do about it?

Our Behaviour

How can we deal with filter bubble?
  • listen to multiple sources
  • be curious
  • look for serendipity
  • seek/look for/follow other points of view – in a safe way (e.g. through private browsing)
  • consider using second accounts
  • re-skin your bubble (this is very intriguing – any explanations/elaboration from the workshop participants?)
How can we teach our students to deal with their filter bubble?
  • train digital literacies
  • teach students to evaluate credibility
  • take them on “guided tours” outside the safe spaces of the VLE/LMS/etc. Work with them in the open spaces of social media, but provide support through collaborative reflection, giving context, etc.
Core message: “I can gain most from my PLN WHEN I triangulate against others”
 

Technical Wishes

Here are some ideas the participants came up with for technical support that could help users with their filter bubbles.
 
These tools & instruments can help me manage my PLN effectively:
  • Option to pay to not be included. if you aren’t paying, then you are the product.
  • “Challenge me” button
  • Avoiding paid-for adverts
  • “Be someone else for the day” button – change your gender in your filter bubble
  • “Forget me temporarily” button
  • Try new things/new perspectives
  • Random search button “I feel lucky”
  • Awareness and how it works
  • Comparing my search results with someone else’s or the results obtained when I’m not logged into the browser on a different network
  • Missing information you would choose to accept (*comparison to peer; *presented different information)

The debate does not end here: please do add your comments/thoughts/opinions/ideas below!

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Attending #ALTC – Workshop on the Filter Bubble

This week I’m attending the ALT Conference in Liverpool. It’s a conference I have been looking forward to attend for years now, and especially with the line-up of keynotes this year (Peter Goodyear, Sian Bayne and Bonnie Stewart), my expectations are sky-high.

I will be giving a workshop on Wednesday afternoon on breaking your Filter Bubble ([1749] – 15.00-16.30 in Elisabeth Gidney I(2)). I chose this topic (apart from the fact that it makes for a catchy title) because I think it is essential that we understand this phenomenon well, to remedy it, in order to save one of the best inventions of the last decades: social media and the online social network. (here’s a very good opinion piece by Paul Mason on this topic)

In many ways, the filter bubble is just a technical consequence of the problem of (information) abundance. Having too much information or content, creates the need to filter and keep ‘relevant’ information. What relevant means, can be debated. Computer scientists use known facts or behaviour of the user to determine what relevance means. The quest to offer relevant information to users goes so far, that it actually creates a cocoon or an echo chamber around the user.

There are many things that can be questioned here: for one, the strategy described above may work for some types of information (such as movies or kids’ toys), but why do we assume it will work for people, organisations or experiences? Another is that the only relation that is sought out is one of similarity – but this is not always the relation that is most relevant (here’s some of my earlier work, where I argue that for learning, we’re actually looking for dissimilar things: http://hdl.handle.net/1820/6818).

I’m particularly interested in looking at the issue of the Filter Bubble from the perspective of your Personal Learning Network(PLN) (a social network that you design intentionally to support your learning). There is a general consensus that to avoid filter bubbles, we need more diversity in our social media feeds – but what determines diversity here, especially in a PLN?

The central question I want to discuss at the workshop is: “What is diversity in a PLN determined by?”

 

by Bill Watterson

 

I want to work towards two concrete outcomes:

  1. a Code of Conduct for online learners, with guidelines to prevent their PLN from becoming a bubble. This outcome needs to complete the following sentences:
    • a) “Your PLN will be most useful WHEN”
    • b) “I can gain most from my PLN WHEN”
  2. a list of desired social media values and functionalities that can prevent filter bubbles. How can we avoid falling into a “small world” context? This outcome creates the following list.
    • a) “These tools & instruments can help me manage my PLN effectively”

To make the most of the workshop it self, I would like to gather some of your feedback on topics and questions that you think we should discuss during the workshop.

The guiding questions that I want to use are centred around things that we can change in our own behaviour, and also things that social media and other technologies need to enable us to do. Some guiding questions to discuss here are:

Our behaviour

  • How do we actively maintain diversity in our PLNs?
  • How do we notice that we are in a filter bubble?
  • What are the actions we can undertake to get out of a filter bubble?
  • What is the role of social media in this?
  • To what extent are social media a cause of filter bubbles?
  • To what extent is the filter bubble a consequence of our own behaviour?

Desired Technological Features

  • What are the values/guiding principles currently underlying social technologies (social media, Twitter, FB, Google search, etc.)
  • How can social media be changed to help us in preventing filter bubbles?
  • Social media – in theory – can bring us in touch with anyone in the world. How do we re-capture this promise of social media?
  • Many companies react to the filter bubble by giving users more ‘control’ over their news feeds. Is ‘control’ the way to go? What is ‘control’?

Are there any questions that you would like to add to this list of topics? Things that you would like us to discuss? Please add them below in the comments section.
This workshop is aimed at educators, researchers and social media users in general.

 

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