Final Assessment Off Cuts

I don’t think this is going to make it’s way into the final assessment script which is a shame. BUT! Nothing is wasted when you have a blog so here we go:

“How do the methods used to generate formal academic knowledge effect it’s representation in digital network media?”

We’re going to look at two apps, Litlong and Curious Edinburgh. Now ostensibly they are representing formal academic knowledge in a very similar manner. They are both apps for mobile phones that add content markers to a GPS map of Edinburgh. However, if you look at them in detail we find that there are vast differences between the apps. I will argue that the cause of these dissimilarities is the different methods the app developers used to generate their formal academic knowledge. From this we can conclude that is important to avoid making instrumentalist conclusions about what methods are used for generating knowledge. In turn, this will reinforce why it is vital to take a critical approach technologies in Education which looks at the wider social practice of a technologies production.

How are they similar?

GENERATE Both are content based apps as they give access to large amounts of data with no assessment or learning task built in. Cherner et al.

GENERATE Both are manipulable – Goodwin and Highfield 2012 no set structure in the order, time or place you access the information. Counters perception of algorithmic/digital culture being committed to procedure.

GENERATE Both required inter-disciplinary approach to their production – interdiscplinary algorthmists relate to interdisciplinary approach of both apps

For a single academic, or even a single department, to produce these apps they would need to be well versed in computer science, data analysis, English Literature, History, Sociology and so on. Far too much for any one person to be an expert in all the requisite fields. This means that the apps had to be produced by an interdisciplinary approach.

The example of these apps provide a possible answer to a question posed by William, namely, should academics learn to code to transmit knowledge? These apps seems to suggest they should just head teams who do the coding for them.


What causes the differences between the apps?

I would argue that the differences between the two apps are caused primarily by the way formal academic knowledge is generated and represented. The developers generated different academic knowledge as they are from different disciplines. This means they would have different ideas about what the content of the apps should be. We can critique this using some of the same methods that Tarleton Gillespie uses to critique algorithms. Specifically, we need to ask how the developers addressed:

  1. Patterns of inclusion
  2. Evaluation of relevance
  3. Entanglement with practice

So LitLong had a very broad pattern of inclusion. As long as the text excerpt found by the algorithm could be mapped onto a location in Edinburgh it was included. Every excerpt that managed this was deemed to be relevant. The workshops with different groups showed the varying ways the app could be entangled with practice.

In contrast, Curious Edinburgh’s pattern of inclusion was far narrower. The locations mapped had to be of verifiable significance to the history of science. The evaluation of relevance was carried out by the academic team drawing upon their knowledge of the field of the history of science. These academics were also in a position of institutional power to ensure it the usage of the app entangled with their student’s practice by making it part of a formal assessment.


What this analysis of the two apps shows is that you can take what on the surface seems like a similar approach and end up with very different ways of representing formal academic knowledge. For me the most significant differences between the two apps were how they were used (in workshops, formal assessments) and the levels of funding they had to produce the apps (with subsequent responsibilities the teams had to their benefactors). These differences were caused by the way the developers chose to generate and represent their knowledge. What is crucial for our understanding of how digital networked media can be used to formulate this kind of knowledge is to consider the social context in which they are made and used. This in turn reinforces the importance of the critical approach taken to Education and Digital Cultures throughout this cours