Today i am sharing some of our ongoing writing for the Learning Science book: this part sits in the central, second, section, and provides an overview of aspects of the ‘hardware’ and ‘software’ in our heads. This is our second draft of this section, as we have been figuring out at what level to pitch this work, and in the broader context of the book. You could really write a full book about any of these things.
In our first collaborative post, back in November 2022, we described the high-level landscape of the multidisciplinary field of Learning Science. In it, we mentioned that Learning Science techniques and tools can be organised into five categories, and in this week’s post, we revisit and expand on the first of them: Learning Science is concerned with how people learn.
1. How people learn:
A description of people’s brains and minds
The first subdomain in our Learning Science model is concerned with the way people learn. It includes neuroscience (how our physical brains and nervous system work, which is sometimes considered ‘The Hardware’) and cognitive science (practical observations and conceptual theories to understand how our ‘minds’ emerge from our brains, sometimes considered ‘The Software’).
As a rough concept, you can envision this category as a description of what happens inside a person when they learn. From this, we glean hallmark areas of study such as:
- Brain structure, neurons, and physiological encoding: This subfield considers the physical structure of the brain, how it functionally enables learning, and how it changes as we learn. Work in this area might discuss ‘neurotransmitters’ (the chemicals that moderate brain activity), ‘functional and neural localization’ (identifying which parts of the brain enable different cognitive activities) or ‘neuroplasticity’ (the brain’s ability to adapt and respond to new learning). We have, ourselves, talked about some of these concepts in our post on ‘Nervous System Sensitivity’, which discussed how stimulation (stress) and individual differences in our nervous systems interact with learning and performance. This study of the ‘hardware’ has accelerated through a succession of ever-more effective sensing technologies, which allow us to observe and interpret, in real time, aspects of what’s happening in the brain. This work has been challenged, in part, because the brain doesn’t work ‘one way’ (like a car engine does), but rather it displays fluid interrelationships between parts, a range of types of interactions, and even intrapersonal differences across time and context.
- Stages of human development: Both neuro- and cognitive scientists (‘hardware’ and ‘software’ specialists) also examine how people develop from infants to adults and eventually into elders – understanding the maturation process, for instance, when and how babies learn to verbalise or mentally manipulate objects, as well as the ageing process, such as the cognitive changes that occur in later life. When we buy a computer, the ‘hardware’ is constant, whilst the software, the Operating Systems (OS) may be updated over time. The brain is not like this: both systems change – the physical structure (as both a function of age and learning) and the ‘software’ (as our knowledge, experience, and strategies develop – something akin to ‘wisdom’ that we discussed earlier). Lev Vygotsky’s cornerstone theory, the Zone of Proximal Development (ZPD), also fits in this subfield. ZPD describes the conceptual space between what we can already do and what we could do with some support (such as expert feedback and scaffolding). Or to put it another way, ZPD describes the space between our capability and our potential. Vygotsky’s work has had an enduring influence, and it’s easy to see why, because it highlights a practical link between understanding of ‘how the brain works’ and just ‘what on earth we can do about it’.
- Memory and forgetting: The study of memory examines things such as ‘synaptic plasticity’ (how memories are physically formed, strengthened, or pruned) and the role that different brain components (such as the amygdala and the emotions it regulates) play in encoding and retrieval. This work is complemented by concepts like the different types of memory (sensory, working, and long-term memory); memory concepts including ‘chunking’, ‘linking’, ‘rehearsing’, ‘reinforcement’, and ‘retrieval’; and studies on the fragile nature of ‘accurate’ memories. (For example, see Elizabeth Loftus’s work on eyewitness testimony or Daniel Schacter’s Seven Sins of Memory.) Another foundational concept in this subfield is the Ebbinghaus Curve, which explains the decline of memory over time and ways to strengthen retention. The study of memory and forgetting is interesting: it sits in a rather practical space and gives actionable insights.
- Information processing theory: Cognitive scientists have developed robust frameworks to explain how we process information. Models also consider the different types of information stores (that is, forms of memory described above) as well as cognitive processes (such as attention, perception, encoding, and retrieval) and executive functions (which helps us understand the mechanisms for higher-order cognitive activities, such as sense making, decision making, and metacognition). Concepts – such as Schema Theory, Cognitive Load Theory, and Embodied Cognition – help us envision how information makes its way from the world, into and through our mental pathways, to be transformed into our own personalised knowledge, and for later integration with other ideas and retrieval for use. And through these lenses, we also better understand the interplay between our ‘minds’ (executive control, System 2) and ‘brains’ (midbrain influences, System 1) – Daniel Kahneman’s Two Systems of Thinking.
Essentially, this subfield attempts to explain what happens when information enters our heads. Starting with our senses – because the brain doesn’t directly ‘experience’ stimuli – be that heat or cold, a good book, or a Bond film. Our ‘sensory organs’ – our eyes and ears, skin and tongue – are stimulated by movement, light and taste, and then those sensations are translated by our brains into ‘perception’, our interpretation and sense making of the signals. Information processing models are also concerned with the mechanisms of storing and retrieving information, meaning that they partially overlap with more focused studies of memory and forgetting.
- Motivation and emotion: In this subfield researchers might explore topics such as how positive emotions (like curiosity and interest) can enhance attention and memory or how negative emotions (like anxiety and stress) can impair learning. Additionally, this area of study examines the facets of motivation, such as intrinsic versus extrinsic motivation, goal setting, and self-regulation. Motivational theories related to drivers and demotivators also fit here, perhaps starting with well-known conceptual models, like Abraham Maslow’s ‘Hierarchy of Needs’ or Frederick Herzberg’s Two-Factor Theory of motivation and organisational ‘hygiene’, and progressing into more nuanced (and rigorous) studies such as core drives (such as curiosity, meaningfulness, accomplishment, and social influence) and other sociological and psychological levers that influence us, for example, how we’re wired to engage in social reciprocity or have a strong bias for internal consistency among our thoughts, values, and actions. Mihaly Csikszentmihalyi’s famous theory of ‘Flow’ – that feeling of being ‘in the zone’ – could also fit in this subfield.
- Expertise: The study of ‘expertise’ examines the stages of learning from novices through to experts (for example, see the Dreyfus model), and it explores the seemingly superhuman abilities of experts, like Recognition-Primed Decision Making and automaticity – when what we’ve learned become a subconscious routines that ‘frees up’ executive cognitive resources for other matters. (This is one of the reasons why our brains can operate so efficiently, on such little energy, because we don’t have to expend conscious effort on everyday matters, like where to place your fingers whilst typing!) Since we’ve limited ourselves to 8 subfields, we might also squeeze ‘Transfer of Learning’ into this bullet. It describes the extension or generalisation of knowledge and skills from one context to another. For instance, when a person learns about first aid from a seminar and then later applies those skills in a real-world emergency or builds upon the knowledge when considering the anatomy of their pet cat.
- Brain injuries and congenital differences: Many of the earliest attempts to understand how our brains work relied on the victims of mining accidents or war – those with visible damage to parts of their brain. Today, the study of how physical injuries and neurobiological conditions (such as dyslexia) affect learning continues, and we’ve added depth to this subfield through the exploration of neurodivergence. Examining those capabilities that make us learn ‘differently’ not only helps us better understand how the brain works, but it’s also changed the conversation – recognizing that there’s no single ‘perfect’ brain structure or way to learn. (You can probably already see why this leads us away from simple and mechanistic perspectives on learning: instead learning is truly a personal and unique affair – but a process that we can nonetheless understand, support, and shape – and we’ll get more into those methods in Part 3 of this series.)
- Theoretical paradigms of learning: Finally, there are a seemingly countless array of learning theories. Some of the best known theoretical paradigms are behaviourism, cognitivism, constructivism, and connectivism. There are also social learning, experiential learning, and self-directed learning theories, as well as an extensive subdomain focused on developmental learning (that is, how we grow and learn from infancy). While these could arguably be grouped under Part 3 (instructional methods), we included them here because they’re founded on theoretical descriptions of how we learn.
The principles outlined here are the foundational tools – the hammers and spanners of – Learning Science. They help us envision what happens ‘inside our heads’ and inform our approaches to instructional design, assessment, and more L&D systems.
Of course, it’s necessary to caution that while Learning Science offers robust insights, our brains are not ‘one size fits all’. And despite our body of knowledge, the brain (and how it learns) remains both one of the most common things to be found on our planet, and yet one of the most complex and misunderstood.