Always look on the bright side of life(stream) – Week 10 summary

Interpretation is the theme this week, wedded strongly to recognition of the need to make space for cognitive dissonance, for the pluralism of truth, for the concurrent existence of multiple and conflicting interpretations.

It emerges, for example, in considerations of what does, or should, constitute restricted content on YouTube. It’s there in questions around whether learning analytics might help or hinder the development of critical reflective skills on learning gain. And of course, it’s readily apparent in responses to the analytics of the tweetorial last week. In my padlet, my point wasn’t to indicate that some conclusions are better than others, though clearly sometimes they are. It was to demonstrate the potential co-existence of varying, contradictory interpretations. In my blog post analysing the data, I argue that it is the stability of data which gives pause, rather than its scope for misinterpretation. The data remains fixed while its meanings change, an ongoing annulment of data and meaning.

In many ways, this seems to conflict rather than cohere with EDC themes. In cybercultures, I questioned whose voices we hear and the ‘black boxing’ of the powerless or unprivileged. In community cultures, I discussed how singularity of voice or shared experience might engender community development. Here, though, I’m finding that interpretation is ceaselessly multifaceted.

Knox (2014) discusses the ways in which learning analytics might be a means of ‘making the invisible visible’. Perhaps this is happening here. The data is visible, where it once might be hidden; this permits a multitude of interpretations to be visible too, where once only the dominant interpretation would have been. Perhaps learning analytics elicits a shift in power.

Or, perhaps, the dominant interpretation has become this multitude of voices. The dissonance is destabilising, and so in the end only the data is rendered visible, stable, victorious.

Or, perhaps, both.


Knox, J. (2014). Abstracting Learning Analytics. Retrieved from