If you spend any time discussing evidence and education you will inevitably, at some point, be called a ‘positivist’. You may even stand accused of ‘scientism’. It is worth examining these claims in some detail because they are generally false and yet they represent quite a successful strategy for those who make them. The edifice of nouny waffle that constitutes so much education research needs an immune system to survive and these terms represent its killer T cells.
Positivism is a philosophical tradition that asserts the supremacy of observation, experiment, logic and reason over other forms of knowing. It therefore rejects metaphysics and religion and tends to take a utilitarian approach to morality. It seems unlikely that many of those who make claims about evidence in education would accept all of these tenets. Instead, it seems that a form of faux positivism has been developed, specifically for mounting attacks on people who try to use evidence in the social sciences. It goes something like this:
Positivists believe that there are clear and specific answers to questions and that these can be established using the scientific method – that the world is black and white, deterministic and can be fully and concretely described. While this is true for the physical sciences, it is a hopelessly naive way to deal with social sciences because social sciences involve human behaviour. Social scientists understand that different interpretations and perspectives mean that there are no simple, easy and generalisable answers.
Not only does this perspective misunderstand the role of the scientific method in the social sciences, it completely misrepresents the physical sciences. To understand why, it is worth briefly examining the second term in the armoury, scientism. This generally means an appeal to science as a source of authority when such an appeal is not warranted. Here is an example:
- A high sugar intake causes health problems
- A sugar tax will reduce average sugar intake
- We should introduce a sugar tax
The first two statements in the argument are scientific in nature in that they are potentially testable. They make claims about the world that could be confirmed or refuted by examining evidence. Collecting this evidence may not be easy. In this instance, you probably could not run controlled experiments and so you would have to look for correlations and these would be contestable. Nevertheless, there is the potential to examine such claims in a methodical way.
The third statement is not scientific in nature. It is about what we should do. This is fundamentally a moral and political question. For instance, there may be those who object to trying to manipulate behaviour in this way, even if they believe it would be effective. Anyone who attempted to claim that the third statement was scientific and that science demonstrates that we should introduce a sugar tax would be guilty of scientism. They have overreached the bounds of science. In this sense, scientism is close in meaning to the proper sense of positivism. And yet those who profess to object to supposed positivism in education research might not even notice the problem in this type of argument.
They may also not notice another issue. When a scientist claims that the evidence shows a sugar tax will reduce average sugar intake, they are making general claims about the population as a whole. They are not making specific claims about exactly how this tax would affect every individual or that the effect would be exactly the same on every individual. It is not necessary to do so. You can still make general claims without specifying what would happen in every individual case.
Similarly, when a reading researcher claims that a particular method of teaching reading is, on average, more effective than the alternatives, it is not necessary for the researcher to be able to specify that this will be true for every single individual or to be able to predict exactly how this method will impact on every single individual. Pointing out that the effects on specific individuals are unpredictable does not refute the original claim.
And it is not just social science that is like this. It is the rule rather than the exception across the sciences. The idea that the physical sciences are completely uniform, predictable, concrete and complete descriptions of nature is just plain wrong.
Science works by creating a model, using that model to make predictions and then performing experiments or making observations to see whether those predictions are accurate or not. A model that is pretty good at generating accurate predictions becomes a theory. Models contain abstractions and are necessarily incomplete descriptions of the world. They often posit the existence of something that cannot be directly observed. Probably the most famous example is Darwin’s theory of evolution which could not explain exactly how genetic information was passed from a parent to a child.
Consider then a model used by cognitive scientists; that the mind contains a limited working memory and an effectively limitless long-term memory. This is the central model of cognitive load theory. It does not matter whether this is a complete description of how the brain works. It does not matter that it is impossible for long-term memory to be literally limitless. It does not matter that we cannot point to the bits of the brain that house these various components. It does not matter that individuals vary in their working memory capacity. None of these things makes this model fail as a scientific model. Crucially, it can be used to make testable predictions and that is really all that is needed.
But isn’t physics different? When it comes down to actual physical matter, can’t we be certain, at least in principle, as to exactly what is going on? Not really, no.
Take the example of the weather. This is a physical system governed by physical laws that are well known. Yet it is notoriously difficult to predict the weather because small changes in starting conditions lead to massive changes later down the line. It is a ‘chaotic’ system. As a result, we are nowhere near an accurate and complete model of the weather, although we have become better at making imperfect models and using these to make reasonable, short-term predictions.
And physics is fundamentally unpredictable at the smallest level. If I fire individual photons of light through a pair of slits and onto a screen, it is impossible, in principal, to figure out where each photon will land on the screen. However, we can accurately predict the pattern that the sum of these photons will make. This is analogous to demonstrating that, on average, one educational approach is more effective than another while not being able to completely predict the effects it will have on each individual student.
Yes, there are still fundamental differences between both kinds of experiment. Social factors that may impact on the results of an educational trial can be far harder to control than the kinds of factors that may impact on a physics experiment involving light, but that does not amount to the need for an entirely different model of what it means to know something or of how to demonstrate whether a proposition is true. It does not mean we can simply reject the all of the accumulated evidence about how people learn by labeling is as ‘positivistic’. Robust, repeatable findings cannot simply be magicked away.