One of the hardest ideas about science to communicate is that we can never be absolutely certain in what it is we “know” through science. We must always be open-minded that our knowledge of the world might be wrong. However, the way that we teach science communicates certainty. This is a problem.
What is knowledge?
The question of what constitutes knowledge is a difficult one to answer. Philosophers today continue a long tradition of discussing what knowledge is. One of the most useful definitions proposed is that knowledge is ‘justified true belief.’ According to this definition, something is known if it meets three criteria: it is justified, it is true, and it is believed.
Something that is known must be justified: there must be reasons (evidence and backing) to support the knowledge, and these reasons must be available to the knower. It is not enough to say something is justified; the reasons must be able to be explored.
Something that is known must be true. It must be consistent with reality.
Something that is known must be believed. If it is not believed, it is not truly “known.” This is an interesting provocation for teachers to reflect upon. How many times have you presented students with “facts” that challenge their existing beliefs, only to have them curiously forget the new facts and return to their old beliefs once the test is over?
For example, if you were to say that your housemate, Jake, was home, your justification for this might be that you saw Jake walk into his bedroom through the bedroom door, and have not yet seen him emerge. Your statement of his whereabouts is true as long as he really is home. And it is knowledge if you really believe that Jake is home. But if he is not home, because he snuck out of the window, or walked out of his bedroom when you weren’t looking, then even though you believe him to be home, and you are justified in this, it is not knowledge. If Jake is home, and there is good justification to assert that he is home, but you refuse to believe it, with or without justification, you do not “know” that he is at home.
So, using this definition, we can say that we “know” something when we believe it, we can justify it, and we can argue that it is consistent with reality.
But what about situations beyond the immediate, the local, and the private? What is real in a physical sense? A social sense? How can we know what is real, when we each fall so easily for illusions, for bias?
The best methods for building models of reality are those of science. Science doesn’t tell us directly about reality, but the processes of science give us models of the physical, natural, and social worlds that we can test and use to make predictions. By rejecting the models that don’t allow us to make accurate predictions, and keeping and extending the models that do, we build a better, more sophisticated model of reality. The processes of science aren’t perfect, but they are better than any other set of processes or guidelines we have for learning what is real.
Science informs our justifications through evidence, and also allows us to have a high degree of confidence that our knowledge is consistent with reality. However, we can never* know something with 100% certainty.
Paradigm shifts in science
In the history of science, there have been times when old models of reality have given ways to new models, because of a burden of evidence, and the predictive capacity of the new model is so much better than the old.
Even in the physical sciences, which describe phenomena and events in the material world, our models have been wrong before. Examples of this include the models of shape of the Earth (hint: it’s not flat), the model of the Solar System (heliocentrism), the properties of atoms (and the notion that they are the smallest unit of matter), the physiology of living things, how the diversity of life arose (evolution), and the composition and motion of the Earth (plate tectonics).
These shifts in models are known as paradigm shifts. These can take a long time, even in today’s technology-saturated context, and there are many arguments between scientists before the burden of evidence becomes overwhelming and the new model is widely accepted.
Paradigm shifts demonstrate that what is known by science is not fixed, while also showing that for a model to shift, there must be a burden of evidence and reasoning to justify it.
In science education, we hope to achieve shifts in our students’ models of the physical and natural world and how it works. To do this, we need to bring students’ existing mental models to the surface, acknowledge them, and challenge them (if they are not consistent with current science models).
Probability and Bayesian judgements
When should we be very confident? There are models of the world that we can be very confident in using for making predictions, and others not so much.
This also depends on the predictions being made. As Marten Koomen, a physics teacher, pointed out in this blog post last week, we have two useful, different, but complementary models for light that explain how it behaves: both a particle, and a wave. Depending on what prediction we want to make (or observation we want to explain), we can use one or both models to help us.
Similarly, we have an old model of forces and motion (Newtonion mechanics) that is sufficient for us to land spacecraft on distant planets, but a newer model (Einsteinian mechanics) which is much more useful at the level of the very big and the very small.
How confident we can be in any model, scientific or not, in being useful for any particular situation, can be expressed in terms of probability and statistics. In multifactorial situations, we can employ Bayesian statistics to calculate probabilities and make predictions.
While one’s confidence in the theory of gravity is not “so loose-weave / Of a morning / When deciding whether to leave / Her apartment by the front door / Or a window on the second floor, (Minchin, 2008)” it is also still not absolutely certain.
So, while we can’t be “100%, absolutely certain” that any of our models exactly match reality, we can be highly confident in using them to make predictions, and highly confident in the predictions we make from them. I’ll continue to use the front door, as I’m very confident in my prediction that I’ll not be so successful in making it to work if I try to leave by the window on my second floor.
Social sciences and black-box investigations
Our confidence in the predictions we make from our models necessarily lessens a little when we move from the physical sciences to the social sciences.
The physical sciences describe the material world, in which there are interactions between physical components of the world. We can predict the size and outcome of these interactions very accurately, and hence confidently, usually quantitatively.
Biology is slightly messier because the emergent property of life brings with it complicating factors that are not always easy to tease out and describe; this is the reason that biology is often referred to as a natural science rather than a physical science. While quantitative data is often gathered by a biologist, qualitative data is also valuable.
The social sciences, such as psychology and sociology, are messier still, because they investigate and aim to describe social and mental events and phenomena rather than physical. Social interactions are initiated and mediated by black boxes (our minds, which we cannot see into or describe very easily) and the environment in which interactions occur. Because these interactions are not so predictable, and are affected by a large number of factors, both natural and environmental, we must qualify our assertions about the social sciences very carefully. This applies to education, too.
While we can still be quite confident in our predictions made using models of the social world, our confidence is not as strong as it is in the case of the physical sciences.
Open-mindedness is a part of science
While we can be confident in the models developed from scientific inquiry, we can never be certain in our conclusions, and we must be open-minded that our ideas might be wrong.
Scientism, positivism, and materialism
Scientism describes a state of excessive belief in science; the belief that science can answer all questions. Scientism holds that philosophical, historical, and social questions can all be investigated scientifically.
Materialism is the belief that all interactions and phenomena are the result of physical interactions between matter, including consciousness and social phenomena.
Positivism is the exclusive use of the methods of the physical sciences to justify knowledge. A positivist holds that the social world operates according to general laws, analogous to the physical world. Often, positivists reject all qualitative evidence. Any other form of knowledge is dismissed. It is an extreme form of empiricism.
These three positions form presuppositions for interpreting evidence and reasoning from it. These positions are problematic, however, as they are all tautological: evidence or reasoning that challenges these positions is rejected by these positions. These are not open-minded positions to take. As such, I would describe them as non-scientific positions.
What does this have to do with teaching science?
Science is often taught as a set of facts that are to be memorised and recalled at the right time (usually on a test). A slightly better format of science education involves teaching students theories (models) that can be applied to specific situations (usually on a test). Both of these styles of teaching and learning are appropriate for teaching the Australian Curriculum: Science. However, both also communicate science as certain, established, and infallible. These styles of teaching fail to teach students effectively about science.
The failure of science education is not that students don’t recall facts or understand theories; it is that they have not been challenged to evaluate the validity of their own beliefs and assumptions about the world.
But I’m willing to be wrong…