One of the criticisms of current data obsessions is the way in which analytic results appear to 'shout science': in other words, to make a claim for objectivity from collective subjectivities. Science is held to be domain of objectivity - of denotative claims: the realm where one points at something and says 'it's clearly moving at speed x, with momentum y" and so on. The subjective world is a world of connotation and interpretation: meaning arises between things. 'Shouting science' makes denotatative claims about connotative processes. In the final analysis, this is an act of oppression: it can only impose an 'objective' judgement on those whose connotative processes might not only come to a different conclusion, but almost certainly come to a different conclusion in the additional light of the imposition of analytic claims to truth.
What's wrong here is not data analytics itself. It is our view of science which is mistaken. To put it quite simply, 'objective' judgements are not indicative of stable entities in the world to which labels can be attached. They are indicative of coherences of understanding and expectation around which action can be coordinated. Zebras have no word of 'lion', but there is a coherence of expectation among zebras that coordinates action when a lion is spotted. We see the effects of these coherences in the regularities of behaviour which occur around particular entities: lifeworld entities like 'lions' exhibit a universal constraint on behaviour rendering it surprisingly predictable.
The question, with regard to data, is whether there is some indicative index of this coherence of understanding. But the same logic applies. Such an indicative index, were one to be available, is itself indicative of coherences of understanding and expectation around which action can be coordinated. However, when things get more abstract, deeper problems set in. A declaration of an index by a powerful person can create a coordination of behaviour driven by understanding and expectations about the constraints of power dynamics, not the index itself. This is where false consciousness begins - and misleading representations of science.
If learning analytics is seen as a set of creative re-representations of things that happen in education, then it can make a more modest claim to add to the numerous descriptions of educational processes we already have. It is another descriptive layer in our connotative process. In a way, it is rather like how poets describe things:
The scientific question concerns the generative power of the imagination and the discovery of constraints that nature imposes on imagined mechanisms. Codified mechanisms are coordinators of discursive processes. That might be the beginning of organised attempts to find those mechanisms in nature: The Hadron Supercollider is a good example. When some mechanisms are not found, the natural constraints which prevent them can also be coordinated. It's not finding the Higgs Boson which is important; it is identifying those speculated mechanisms which cannot be found.
Our research approach to education would look very different if we applied this approach. We would identify the constraints within which theoretical constructs are upheld, and where they don't work. Most importantly for the data analysts, the appreciation of constraints requires the accretion of many different descriptions. It is in exercising a connotative judgement that understandings between us can be coordinated.
In a deep way, science IS education.
What's wrong here is not data analytics itself. It is our view of science which is mistaken. To put it quite simply, 'objective' judgements are not indicative of stable entities in the world to which labels can be attached. They are indicative of coherences of understanding and expectation around which action can be coordinated. Zebras have no word of 'lion', but there is a coherence of expectation among zebras that coordinates action when a lion is spotted. We see the effects of these coherences in the regularities of behaviour which occur around particular entities: lifeworld entities like 'lions' exhibit a universal constraint on behaviour rendering it surprisingly predictable.
The question, with regard to data, is whether there is some indicative index of this coherence of understanding. But the same logic applies. Such an indicative index, were one to be available, is itself indicative of coherences of understanding and expectation around which action can be coordinated. However, when things get more abstract, deeper problems set in. A declaration of an index by a powerful person can create a coordination of behaviour driven by understanding and expectations about the constraints of power dynamics, not the index itself. This is where false consciousness begins - and misleading representations of science.
If learning analytics is seen as a set of creative re-representations of things that happen in education, then it can make a more modest claim to add to the numerous descriptions of educational processes we already have. It is another descriptive layer in our connotative process. In a way, it is rather like how poets describe things:
Understanding is built up from the accretions of references. So too might we build up an understanding of learners through accretions of representations of what they do. The Facebook analytical graph is one of many possible descriptions of interactions online. It may be that such representations are important because so few alternative descriptions of online behaviour are available. In face-to-face communication we have bodies, sounds, movements, smells, touch and so on. Each contributes a layer of the connotative experience. Is it a wonder that online we feel the need to create this diversity of description?
But then if we do this, and we assert it as denotative, a distortion occurs. But we only do this because we think this is what science does.
The scientific question concerns the generative power of the imagination and the discovery of constraints that nature imposes on imagined mechanisms. Codified mechanisms are coordinators of discursive processes. That might be the beginning of organised attempts to find those mechanisms in nature: The Hadron Supercollider is a good example. When some mechanisms are not found, the natural constraints which prevent them can also be coordinated. It's not finding the Higgs Boson which is important; it is identifying those speculated mechanisms which cannot be found.
Our research approach to education would look very different if we applied this approach. We would identify the constraints within which theoretical constructs are upheld, and where they don't work. Most importantly for the data analysts, the appreciation of constraints requires the accretion of many different descriptions. It is in exercising a connotative judgement that understandings between us can be coordinated.
In a deep way, science IS education.
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