This map makes visible a geography of academic achievement with inherent class and race implications and shows educational achievement as both marking and constructing divisions in society.
Mapping the geography of academic attainment
Big data can be harnessed for good or wielded as unimpeachable fact to direct our action in pursuit of anothers’ gain. As Enyon states,
as a community we need to shape the agenda rather than simply respond to the one offered by others
(Enyon, 2013, p.238)
Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology. pp.237-240.
Just Pinned to Education and Digital Cultures: Big Data Humor: Power of the Pie http://ift.tt/2moCA7p
I pinned this image to Pinterest because it is amusing but also because it says lots of things to me about Learning Analytics and infographics. Learning Analytics companies are keen to promote their ‘dashboards’ where information is depicted graphically, enabling users to understand performance, trend or numbers at a glance. I am suspicious of these kind of duplo infographics which resist all nuance to deliver headline news. The visual representation of statistics is, by definition, devoid of intricate detail, but the view of information these graphics embody is more akin to advertising a reality than reflecting one. Infographics are selling us a view of information, the gathering of which has ready-encoded in it the decisions and motives behind the view it wants to promote. Advertising is designed to be appealing, persuasive and agentic whilst subtly constitutive of a social and political stance.
I pinned this cartoon to Pinterest because it made me think about learning analytics and its requirement to model the student and map the knowledge domain against which she might be measured. Williamson emphasises the importance of modelling,
complex human and social activities – and the values and assumptions held about them – are operationalized by being translated into a functional interaction of models, goals, data, variables, indicators, and outcomes. The algorithm itself, in this sense, may not be as important an object of inquiry as the underlying ‘models’
(Williamson, 2017, p.84)
Does modelling de-humanise the student by reducing her to data and, even if that is the case, are hitherto unrealised patterns of behaviour revealed which might help her? The more detailed and recursive the modelling, employing techniques of machine learning, the more faithful the model and potentially, the more useful a prediction or prescription. However, as Siemens (2013) admits, analytics is “about identifying and revealing what already exists” (p.1395), leaving little scope to unearth the accidents and epiphanies of learning.
Just Pinned to Education and Digital Cultures: Data dehumanises? http://ift.tt/2mJqI2u
Just Pinned to Education and Digital Cultures: Business Intelligence Dogbert style http://ift.tt/2mokKkT