Learning Science: a #WorkingOutLoud post

I started work today on the full Modern Learning Capability Programme, with the module on Learning Science. As part of #WorkingOutLoud, i am sharing this work here, although please note that this is still early stage, so not perfect. Specifically, this piece stands at the start, and tries to deconstruct the notion of ‘learning science’ into a view of the varied specific scientific subjects that may contribute to this discipline. It also starts to address what a real science is, and how we find rigour, not hype.


In this module, on Learning Science, i will try to help you set your own foundations for study and understanding. We will deconstruct the term ‘learning science’, to understand the separate disciplines that it draws upon. We will also consider what we mean by ‘science’ and the ways that it may give us strength in our work, as well as ways that that strength may be lacking.

Inevitably, to understand science and the scientific approach, we have to delve into some theory, but our outputs will be practical: finding your own platform for learning and understanding, and giving you the framework through which to judge the things you read and see in the name of ‘learning science’.
What is Learning Science?

Science represents a systematic approach to building knowledge: it is the study of the world through observation and experiment. It is not specifically a book of outcomes, but rather the methodology by which we come to those conclusions: science can provide us with an answer, but typically provides us with further questions too. Science is the journey as much as it is the destination.

When we talk about ‘Learning Science’, we are considering that there is a systematically organised body of knowledge related to the ways that we learn. In common with any scientifically derived knowledge, we assume that there is an evidence base behind it, and both active, and ongoing, work to expand it. Science is never ‘done’.

Science is both an intellectual activity, and a practical one: you can ‘think’ about things, or do them, both under the heading of science. An experiment is exactly that.

Whilst in popular discussion, we talk about ‘good’ and ‘bad’ science, it is not specifically the methodology that is valued judged, but rather the shape of the experiment, or the interpretation of the results. Good scientists can do bad science, because science is typically ‘confounded’.

Imagine you stand on a hill, looking across a valley, in search of the sea (representing the quest for knowledge as a landscape may be a tired metaphor, but i hope will illustrate a point): your view may be clear for thirty or forty miles. You may see mountains to the north, and the sea to the south. Or it may be hazy, your view occluded by the morning mist in the valleys, or by nearby trees, or tall buildings. It may be cloudy, rainy, or simply the landscape may be lost in the dark of night. Or, of course, you may be looking in the wrong direction entirely.

Much of the history of science, in whatever discipline, represents the blind wandering in this landscape: sometimes finding an interesting destination, often lost, sometimes in danger of thinking we are found.

How we know things

Consider this section a foundation piece: it explores how we know things, and sets the context for learning science.

Perhaps surprisingly, we are not entirely sure how we know things at all: the philosophical exploration of this is known as epistemology, and it presents us with a number of perspectives. For our purposes though, i want to consider just two elements: some types of knowledge are derived from ‘what comes before’, and some are founded upon experience and empirical evidence.

These two types of knowledge are called ‘a priori’, and ‘a posteriori’: a priori represents what comes before, and a posteriori represents what we learn (through science and experience).

Maths is a typical example of a priori knowledge: the reasoning that 2+2=4 is independent of personal experience. The field has grown through deduction and pure reason. Consider this ‘theoretical’.

By contrast, a field such as biology, or astronomy, which has grown through experiment and observation, hypothesis and conclusion, is a posteriori. Note that it is within this space, a posteriori, that empiricism sits.

Within this frame of understanding, perhaps most of what we consider to be ‘Learning Science’ will be a form of a posteriori knowledge.

In my understanding, this is important, because it speaks to how we will learn more about learning: not through pure reason and induction alone, but rather through experimentation and experience. Learning is a very human science.

Types of science

Consider this section as setting up the different ‘boxes’ we will explore further. So it sets more context, but also introduces some specialist topics that we need to explore.

Considering ‘Learning Science’, our focus will be on the empirical sciences, which can typically be split into three areas:

  • The Physical science – chemistry, astronomy, metallurgy, etc which concern the science of the material world.
  • The Biological Sciences – zoology, genetics, palaeontology, molecular biology, physiology etc, which concern living things.
  • The Psychological Sciences (sometimes called ‘Social Sciences’, or even ‘soft sciences’) – psychology, sociology, anthropology etc. Sometimes economics is included in this, although there is debate as to whether it is a science at all.

You should note that there are about as many different taxonomies of science as there are people writing about the taxonomy of science, but this gives a flavour.

A related description is worth noting too, which considers the cognitive nature of these in approach: the physical sciences can be described as building remote types of knowledge, whilst both biological and psychological sciences are said to build an intimate type.

Types of Science relevant to learning

Here, we consider which types of science are relevant to ‘learning’, and deconstruct the idea that ‘learning science’ is a separate discipline in it’s own right.

When we consider ‘Learning’, we may not actually mean one field of science in it’s entirety, but rather a programme that draws upon a number of different fields of science and, hence, different approaches.

To explore modern learning science, we should start by looking in some of the following areas, starting with the psychological sciences:

  • Psychology – which is the science of behaviour and the mind. This is a very broad discipline that we shall delve into much further. We may draw upon psychology in a wide number of ways, for example, to understand how we think about things and construct our understanding of the world, how bias is held, personality structure, concentration and engagement, resilience, relationships, even communication and motivation (and many more!).
  • Sociology – the science of social relationships and interaction. Culture sits within this space as a study of the individuals and collective behaviours.
  • Anthropology – the scientific study of humans, human behaviour, and society. A lot of work on cultural meaning sits here, in particular how ‘norms’ are established.
  • Linguistics – the study of language is relevant to understand meaning, and the ways that we share it (e.g. learning). For example, many of the social forces such as ‘trust’, ‘pride’, ‘respect’, ‘empathy’, etc, have underlying cultural contexts which can vary by region.
  • Economics – my own work is increasingly influenced by aspects of economic theory: it’s a field of science that explores the production, distribution, and consumption of goods (including knowledge goods, hence learning).

We could also have included some more distant social sciences that bring a perspective to learning (and i will try to indicate why they are relevant):

  • History – can be considered a social science, and it relevant in the context of learning in global organisations, where we discuss cultural differences, social norms of groups (e.g. views on how people in different regions learn), motivation and drivers etc.
  • Political Science – which may provide a lens for considering the flows of power, and social forces of difference, as well as a lens through which to understand change.

Alongside these social sciences, there are aspects of the biological sciences that may concern us (and again i will try to indicate why):

  • Neuroscience – the structural scientific study of the nervous systems, and our earliest attempts to understand how it works.
  • Anatomy – any exploration of movement in learning, as well as understanding virtual reality, may involve understanding of anatomy.
  • Ethology – a study of animal behaviour, which includes all sorts of work on stimulus and response, hence reward mechanisms.

Beyond this, if we were being technical, we could even consider the sciences that drive specific learning technologies: artificial intelligence, machine learning, headsets and handsets, but here we are focussed more specifically on learning design and behaviour (and will explore some of these other areas in other modules).

Before we disappear down the rabbit hold of the taxonomy of sciences any further, i would encourage you to try to draw out two key themes:

  • Firstly, what will you include in your own ‘Learning Science’: if you include neuroscience, but neglect cognitive science, where will that leave you?
  • Secondly, what will you leave out: to understand science, we have to understand that failure often comes from our own blindness to confounding, or contributing, factors. So being deliberate about what we are NOT studying can be as important as considering what we are.

Empirical Knowledge

When we make a hypothesis, we are making a (hopefully informed) statement about how the world is. We then carry out research, and shape an experiment, to verify, or repudiate, that claim. In that context, ‘empirical’ evidence is what we see that either validates a truth (what we hypothesised), or falsifies it.

Primary evidence is that we we directly observe, and publish. Secondary evidence is an exploration of that which has been published, and analysed, by others.

There are some key aspects of this which are important to consider:

  • When we formulate a hypothesis, we are making a statement, based upon our current understanding, of how something is. BUT we are not stating a truth just yet.
  • We test that hypothesis, and the validity of our science lies in how we carry out that test. One way to consider this is to think about an annual service that you carry out on your car: it tests and verifies a number of factors that are important to safety and longevity (oil levels, filters, emissions etc). But the test is only valid (as a measure of safety) if, for example, it checks the wear on the tyres. A badly designed experiment would fail to do this.
  • We interpret the evidence that we have gathered, and on the back of the hypothesis, and experimentation, we draw out the meaning, and make our conclusions.
  • We submit these to peer review, and publish them. Peer review, the value free critique by our peers, is the foundation of quality here: they ensure we have designed our study well.

Where ‘Learning Science’ fails

As should be clear, there are hence a number of ways that we can fail, not least of which:

  • We may formulate a hypothesis that fails to account for an entire field of existing knowledge.
  • We may design a poor experiment, or there may be a fundamental inductive flaw in our reasoning for the outcomes we observe. For example, i may decide that engagement in compliance training is a feature of reward, but if i have not tested that assumption, it is simply supposition.
  • We may introduce personal knowledge bias, or even Organisational political bias, into our interpretation.
  • Without peer review, we lack the external moderation of our efforts.
  • Without publication, we fail to contribute to the broader discipline, and also lack opportunities for feedback.

About julianstodd

Author, Artist, Researcher, and Founder of Sea Salt Learning. My work explores the context of the Social Age and the intersection of formal and social systems.
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5 Responses to Learning Science: a #WorkingOutLoud post

  1. Pingback: Learning Science: #WorkingOutLoud on Philosophical Approaches and Science | Julian Stodd's Learning Blog

  2. Pingback: Learning Science: Neuroscience | Julian Stodd's Learning Blog

  3. Pingback: What I’m Thinking About: Domains of Knowledge | Julian Stodd's Learning Blog

  4. Pingback: Learning Science: The Problem With Data, And How You Can Measure Anything | Julian Stodd's Learning Blog

  5. Pingback: Learning Science: Mapping Learning | Julian Stodd's Learning Blog

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