3 key principles from learning research
What really works in learning? To find the answers to this we carried out a massive research project to seek out the evidence from learning research, cognitive psychology and neuroscience. We’ve turned that research into a set of practical learning design principles to support anyone designing online courses – and that includes us, in our work as a learning agency.
To find out what we discovered, read on. If you want to go straight to the research whitepaper, you can get it here.
Why we need research-informed learning design principles
Too many online courses aren’t based on any real learning methodology, with many being little more than information products or an in-person course dumped into an online platform. We want to help change that, and we want these learning design principles – as well as our own work – to be part of the solution.
Whatever the type of course and subject, whoever the learners, and whatever the budget, a learning designer needs to be able to answer these questions:
- What is truly critical in designing an effective learning experience? And how do we know this?
- How can we make sure that research-informed best practice underpins everything we do?
- How can we use new digital tools to best advantage – and not merely use technology for its own sake? (a.k.a. ‘shiny toy syndrome’)
Three key principles for effective learning
There have been mountains of research from a wide variety of perspectives into what really works in learning. But despite the range of different tools, methods and environments studied over the years, three key insights consistently emerge loud and clear.
To design an effective adult learning experience, we need to:
- understand the learner’s context (past, present and future);
- establish and respond to the learner’s existing level of knowledge; and
- provide adequate and appropriate practice over time.
Let’s take a closer look at each of the three principles.
Design for the learner and their context
‘Context’ is more complicated than it seems. It has a physical or environmental dimension, but it’s also cognitive, emotional, social and cultural; and all of these are interconnected.
‘Context’ has more than one perspective
The concrete, environmental perspective
Learning can happen in many contexts, both formal and informal – it’s not restricted to traditional classroom or teacher-led settings. We can learn by ourselves or in a group, at home or at work, online or face-to-face.
And physically, anyone on any given day may be tired, hungry, alert, excited, and so on.
All of these affect and interact with our effort, engagement and experience of learning.
The internal perspective
Our ‘learning context’ includes our goals and motivations, and also our capacity to pay attention, our memory and our emotional state. We need to be focused when studying and to see the value in what we’re learning. Some researchers even argue that motivation is the most important factor for success, regardless of any inherent aptitude.
The social perspective
Learning is not a purely mental activity: all learning is embedded in its social, cultural and historical context.
Looking at the social dimension, we can benefit from interacting with other learnings – supporting and learning from one another.
And culture can be an inportant factor: ideas and methods which seem appropriate and useful might actually turn out to be culturally unacceptable to some learners.
In short, the whole person and the whole environment are relevant to effective learning. It’s not just a question of transmitting information from expert to novice; as learners, we need to understand how the process works and how it relates to our own goals, experiences and identities.
Our ‘learning context’ includes our goals and motivations, and also our capacity to pay attention, our memory and our emotional state. We need to be focused when studying and to see the value in what we’re learning. Some researchers even argue that motivation is the most important factor for success, regardless of any inherent aptitude.
Learning happens in the brain, the body and beyond
Here’s a question: when is a ‘learner’ not a learner?
OK, trick question. But the point is that so-called ‘learners’ are, of course, people first and foremost. Just because they are currently sitting in a particular class or using a particular app doesn’t mean that all the other things going on in their brain, body and life in general are irrelevant or forgettable. And this wider context can significantly influence learning success.
For a learning experience designer, understanding a person’s micro and macro contexts is the first critical step in designing something that will work well for them individually.
We learn by paying attention and building networks of knowledge
Common ideas of learning often start with the role of the brain, and especially memory. When planning and monitoring the effectiveness of any learning experience, we may well wonder: what actually happens (or doesn’t happen) inside a learner’s head?
The world is full of stimuli which we somehow need to selectively filter and pay conscious attention to. And while there are many theories about how information moves from working memory to long-term memory, there’s still no widespread agreement among researchers on the exact processes involved.
What we do know is that by filtering, attending and connecting the stimuli we encounter, we reinforce and consolidate networks of knowledge in our long-term memory. These networks are known as ‘mental models’ or ‘schemata’, and they ultimately guide our perception, our decisions and our actions.
This means that, in order to build up solid mental models, we need to focus and control our attention somehow while learning.
The brain has limited processing power
Our brains can only process a small amount of information at a time, and if we exceed its capacity to pay attention, we also risk unmanageable cognitive load, and that will impair our ability to learning.
Unlike long-term memory, the capacity of working memory is limited. Most people can process no more than about five ‘interactive elements’ simultaneously. As defined by Chen et al (2018), these are “elements that must be processed simultaneously in working memory as they are logically related”.
Our existing level of knowledge affects cognitive load
If our existing knowledge level is low, content might be too unfamiliar or complex, requiring lots of new things to be processed at the same time and overloading our working memory.
But even when we have a good level of existing knowledge, content can be presented to us in a way that makes it feel more unfamiliar or complex; or in a way that makes it very difficult to figure out what to focus on.
So it’s important to help learners manage cognitive load.
Avoid seductive details
Learning materials often include ‘seductive details’ – details or information that are attention-grabbing but irrelevant. These might make a textbook look more appealing, like a magazine, or seem ‘fun’ to someone who just glances at an app without seriously trying to learn from it; but details like this take up valuable attentional resources. And this is especially true if we don’t already have enough existing knowledge to help us work out which details are actually relevant.
Provide a single focus of attention
Multitasking is a myth. For conscious learning, a single focus of attention is really important. When we feel that we are managing to split our attention effectively, this is probably because one of the things we’re focusing on is already well-known and/or automatic. But we’re often inaccurate in our judgments of how many things we can focus on simultaneously.
This point has been made very dramatically by road safety campaigns:
Of course, the consequences of split attention during a learning experience are probably not quite as dangerous as split attention while driving! But the underlying mental processes are the same, so it’s important for learning designers to remember: we should be careful not to allow any feature, game or interaction to be so distracting that it obscures the learning content.
Motivation is key
Psychologists will confirm that we need a sense of purpose in life, a feeling that what we’re doing is worthwhile – and this is just as true for a learning experience. Motivation is such a powerful influence on learning that some researchers argue that it is the most important factor for success, more important even than pre-existing ability.
Not all motivation is equal
We need to see the value in what we’re learning, and intrinsic motivation – an inherent interest/enjoyment in the subject – is generally a much better incentive than extrinsic motivators such as rewards, punishments, external coercion or career reasons.
We need to develop intrinsic motivation
Our learning goals are more likely to feel intrinsically motivated when they’re in line with our other values and needs. And if we’re feeling negative about a learning experience – bored, uninterested, frustrated – we can make it more appealing and manageable through the use of emotional self-regulation strategies such as ‘cognitive reappraisal’.
For example, imagine someone whose company requires them to attend a training course. They may not be wild about this idea – perhaps they are already too busy at work or don’t believe the new skills they’re learning will actually help with their day-to-day work. By recasting it as ‘an opportunity to improve my memory’ or relating the new skills to future promotional/employability prospects, they can improve their performance without any significant cognitive cost.
We need to be self-determined
Motivation and success in learning are also influenced by how ‘self-determined’ we are. This means we need:
- to understand how our goal and the steps involved in achieving it relate to our real lives;
- to believe in our own ability to achieve this goal;
- to exercise some degree of control in directing our own progress towards our goals.
Perseverance is the key to success
Learning isn’t always easy. We face difficulties and challenges along the way. There has been a lot of research focusing on conscientiousness, ‘hardiness’ or ‘grit’, and some learners may well be sustained through challenges by these personal qualities.
The key factor in all these cases is actually perseverance of effort. A lot has been written about ‘grit’, for example, which is essentially made up of two things: perseverance in the face of obstacles and consistent application to learning something (rather than frequently changing interests). Reviewing the research suggests that perseverance is the truly critical one.
Successful learners will be those who keep going – those who feel driven to finish what they’ve started, even when it’s hard. It’s important not to give up at these moments. Even when we’re genuinely interested in learning something, there will be some tough moments when we won’t feel especially enthusiastic. But if we persevere, if we continue making an effort even at those times when our interest is waning, if we take a deep breath and then keep working away nevertheless, it is this perseverance that will help us ultimately succeed.
Learning is not all in the mind
So far, we’ve mainly considered what goes on within an individual learner. But it would be a big mistake to forget that:
“Brains are in bodies, bodies are in the world, and meaningful action in these worlds is in large part socially constructed and conducted.”
Like so much of human experience, learning is significantly influenced by social and cultural context. The cognitive, emotional and sociocultural dimensions of learning are all interrelated. As one researcher points out: “Not only are emotions often given expression during interaction in social settings, but it is often social settings that give rise to emotions.”
Unfortunately, a lot of approaches to learning design don’t do justice to the fact that learning is not all in the mind.
Learning is influenced by social and cultural norms
Globalisation is a widely-recognised phenomenon, but the nuances of its influence are often unappreciated: what cultural baggage does a particular learning experience carry? What expectations do the designers have about how people will engage with the experience? What underlying, unquestioned ideas do they have about what ‘learning success’ actually looks like and how to measure it? How can social norms and personal biases affect practical approaches to learning design?
As learning experience designers, we should always be careful to check what implicit assumptions we’re bringing to the design process, as ideas that seem perfectly appropriate and useful might not be unacceptable to learners in some contexts. The adage ‘think globally, act locally’ remains very sound advice.
The learning environment is a social context
Through peer support, one person’s skills or strengths can support the development of those abilities in others. This can help learners to become more autonomous, able to achieve things independently which they weren’t previously able to do without help.
And in settings where we learn in teams (such as at work or in sports), effective collaborative learning allows for shared cognition, active listening, consideration of a range of alternatives and pooling of individuals’ various abilities. What’s more, when the success of individual goals or tasks actually depends on teammates’ own successes, this improves social cooperation and a sense of shared responsibility. Essentially: everyone succeeds when everyone succeeds.
So there you have it. Principle #1 of designing an effective learning experience: understanding the learner’s context is key. The whole person and the whole environment influence learning, not merely the subject matter, content and instructional approach.
The learning experience will vary greatly according to a learner’s cultural norms and expectations, their emotional state, their prior knowledge, motivation(s), commitment, self-awareness, aptitude for a given subject or skill, and their relationship with other learners.
Effective learning is not simply a question of transmitting information from expert to novice; learners need to understand how the learning process itself works and how it relates to their own context, experience and identity.
Respond to what the learner already knows
An effective learning journey has a clear destination and a clear origin. How can you plot the best route from A to B if you haven’t defined either one?
But many domains of learning are not linear and don’t just involve adding one new ‘piece’ of knowledge to another, step-by-step. The reality is that we’re likely to get a little lost on the way, backtracking occasionally, checking the map and retracing our steps. We’ll meet challenges that we’ll have to overcome in order to keep moving.
How can we guide learners through uncharted territory?
Research suggests that we should:
- Establish early on what the learner already knows (if anything) about the topic. Their existing knowledge (or prior mastery of a skill) will affect the focus of their attention and can help with both memory and recall.
- Design learning tasks which get gradually more cognitively demanding. That means starting with more support and direct instruction for learners who are starting with less existing knowledge; then, as knowledge improves, provide more opportunities for independent discovery and practice.
- Let learners themselves make meaningful connections between what they already know and what they are learning – support them, especially at lower ability levels, but don’t just give them all the answers.
- Help learners to know themselves, not just to know the content. This means raising their awareness of what they already know and can do, of how the learning process works, and of their own attention, motivations and emotions– as well as how to monitor and regulate these. This is know as metacognition.
Self-awareness in particular is critical for adult learners, who are often learning outside formal education structures. In a world where ever more adult learning experiences are taking place in online or distance contexts, it’s absolutely vital that learners can manage their own learning effectively.
What you know helps you grow: The importance of a strong foundation in learning
Learning is rarely a simple, linear matter of adding ‘new’ to ‘existing’ knowledge. Multiple connections are forged and reinforced over time between the many different things that we know, both about the subject matter and about the learning process itself. Knowledge accumulates in such a way that the whole is greater than the sum of its parts.
So, to design an effective learning experience, we have to begin with an understanding of what ‘parts’ of learning are already in place, and then support the learner in building on these and developing a richer understanding. Learning is partly about increased breadth of knowledge, but also about increased depth.
Begin at the beginning
Our focus of attention, as well as our ability to both remember and recall new information, are all affected by what we already know or are already able to do. When we have existing knowledge or skill in a subject, we are better able to focus attention in the right place when studying. And that means we can better encode and retrieve new things in our memory.
Generally, the more prior knowledge we have, the more we learn. In particular, tasks that are more cognitively demanding (e.g. making connections between concepts, analysing, explaining) are easier when we have more prior knowledge. The development of ‘higher-order’ cognitive skills depends on first mastering ‘lower-order’ skills.
Start by establishing current levels of knowledge
It’s very important at the start of a learning experience to establish early on if a learner’s prior knowledge is very limited, inaccurate or non-existent. But even these learners have potential, and it’s important for learning designers to resist the urge to be too helpful. Research shows that learners benefit most from the act of generating their own connections between what they already know and what they are learning, potentially resulting in an ‘Aha!’ moment, rather than just looking up the answers or having someone else tell them.
It’s also important to allow sufficient room for learners to develop individually. In Principle 1 we explained that collaboration with peers can be beneficial to learning. But working in pairs or groups can lead to individual thought processes and knowledge retrieval being interrupted or overridden by peers. And that can make it hard to see which learners actually knew what, which can misleading the teacher or learning designer.
Try it out for yourself
Not convinced? Here’s a quick experiment for you to demonstrate the effects of prior knowledge/ability in action.
Look at the word on the left below (Picture A) for 2 seconds, then cover it, take a pencil and piece of paper, and copy down exactly what you just looked at. Now do the same for the other word (Picture B) on the right.
Don’t spend more than 30 seconds trying to copy each one (so 1 minute in total—set a timer if you need to!).
Assuming you know English but not the other language, you probably found that your version of Picture A is quite accurate, but that it was much harder to copy Picture B accurately. That’s because you couldn’t hold its complex visual detail in your working memory – because you’d never seen this configuration of strokes before. You had prior knowledge of holding and directing the movement of a pencil, but no prior knowledge of the letter-shapes of this other language.
Prior knowledge determines how challenging learning will be
Research has shown that people who already hold complex items in their long-term memory are better able to hold them in their working memory. In other words, the capacity of working memory decreases as the complexity (due to unfamiliarity) of the stimulus increases. Your prior knowledge and skills can make the task considerably more or less challenging.
This is often demonstrated by experiments in which participants are briefly shown Chinese characters. Participants who are literate in Chinese already have representations of these characters in their long-term memories, so they’re better able to remember accurately what they’ve just seen; but participants who can’t read Chinese have more difficulty. When participants are shown symbols that are unfamiliar to all of them (e.g. nonsense figures), these differences disappear.
Different approaches work for different levels of prior knowledge
Anyone with classroom teaching experience will know how hard it can be to pitch and pace a lesson appropriately. How best to support and scaffold learning for the weaker students while also avoiding flying ahead at the speed of the stronger ones and leaving everyone else unmanageably over-challenged?
While good intrinsic motivation can overcome the limitations of general aptitude, the nature of the foundation on which we’re attempting to build is very important.
‘Desirable difficulties’ help us learn
All learners need to be faced with ‘desirable difficulties’ – in other words, tasks which are cognitively demanding enough to allow them to extend and deepen their existing knowledge, but without overwhelming them. But interestingly, this is particularly true of those learners with more prior knowledge. So while we might instinctively associate ‘challenge’ or ‘support’ with learners who are struggling, in fact the stronger learners need it just as much. They might just need a different kind.
Learners with less prior knowledge need direct instruction and ‘scaffolding’
Research suggests that learners with limited or no prior knowledge will benefit from more direct instruction. This means explicitly showing connections between things, explicitly explaining new terms, and so on – not just expecting them to work it out unaided.
Learners with low prior knowledge will also benefit from more ‘scaffolding’. It’s important not to overload their working memory by making them focus on too much new information at once, so it’s a good idea to provide help organising this information and/or integrating new content with what they already know. For example, we can use advance organisers, such as concept maps, diagrams or simple narratives/stories related to learners’ real lives, or with complete or partially-complete worked examples.
Learners with more prior knowledge need more independent practice
For learners with more prior knowledge, research suggests they can benefit from more independent practice of figuring out connections between concepts. They can also handle more cognitively demanding tasks, such as analysing, explaining and drawing conclusions.
All learners benefit from a gradual increase in cognitive demand
Learning tasks should be clearly structured and sequenced, and content regularly reviewed. Tasks should increase gradually in their level of cognitive demand, yet be designed and delivered in a way that minimises cognitive load (as mentioned in our blogpost on Learning Principle 1).
Learners benefit from being aware of how they learn
It’s not enough for a teacher or learning designer to know what someone can already do at the start of a new learning journey. Learners themselves need to be aware of the extent and limits of their existing knowledge and abilities before they can build on these effectively.
When learners have a good understanding of the learning process and their role in it, it’s easier to develop good learning strategies and goals. They can improve this understanding by:
- testing themselves and identifying any outstanding gaps in their knowledge;
- taking time to reflect on what learning strategies that have worked for them and why;
- considering the relevance of what they’re learning to their real life – why they are learning a particular thing and how this relates to the bigger picture of what they are trying to develop.
Effective learning involves meta-learning: being aware of our own thinking and behaviour in the learning process. We learn better when we:
- understand how learning works;
- actively control our own attention;
- develop awareness of our own knowledge gaps (for example, by testing ourselves);
- explicitly connect learning content to our own personal identity.
This self-knowledge contributes to the development of learner autonomy. That’s especially important for adults learning outside formal education structures and, increasingly, in online / blended contexts.
Nowadays people everywhere are finding themselves needing to learn, re-skill and up-skill, often in online or distance contexts without regular face-to-face tuition or support, so it’s absolutely critical that they can manage their own learning effectively.
Provide effective practice over time
When does ‘practice make permanent’? Here’s what we know from learning science
We’ve all had the feeling at some point in life that, no matter how much we study something, it simply doesn’t stick. But why is this?
There could be several things behind this:
- You may have studied a lot, but have you actually practised a lot?
- Did you cram all your practice into one or two mega-sessions?
- How hard did you really try to remember something before giving up and googling it?
- Have you ever wondered… am I doing this right?!
Learning and memory are inextricably linked
To encode and store new ideas in long-term memory, we need to encounter and use them many times, ideally in real-life conditions. And most online learning experiences and courses simply don’t provide enough practice.
Once knowledge is assimilated into long-term memory, we can retrieve it and connect it to new content, contexts or problems – and this continues the process of learning. Having a store of knowledge in long-term memory means that working memory capacity and attention can be freed up to process new things, which is extremely important for managing cognitive load and not becoming quickly overwhelmed.
Testing builds memory
Why do we sometimes feel that, no matter how much we study something, it simply doesn’t stick? Well, research shows that testing our memory is more effective than simply studying the content again and again. What’s critical is that learners actively try to remember what they’ve learned.
There are different types of knowledge
The word ‘learning’ is often used to refer to declarative knowledge (what to do); but there is also procedural knowledge (how to do something). And in combination, these contribute to the development of complex skills.
This distinction matters because different types of practice are suited to different types of knowledge. Research shows, for example, that studying and practising little and often over time (‘spaced repetition’) is ideal for declarative knowledge – facts, figures, dates, and rules – where the goal is essentially one of memorisation. But more complex skills, like learning to knead dough or to use a handheld gaming device, require a combination of declarative and procedural knowledge. In this case, spaced practice will help at first for remembering the elements or steps involved; but a more experiential learning process (‘learning by doing), involving intensive practice sessions, will help make these new skills more automatic.
So for an effective adult learning experience, it’s important to know what exactly is the focus of learning and to choose appropriate learning methods and tasks accordingly.
Let’s take a look at this third principle in a bit more depth.
Studying isn’t enough – we need deliberate practice
To move new ideas from our working memory to our long-term memory, we need to encounter them many times, ideally in real-life conditions. But to truly improve at a particular skill, it’s not enough to mindlessly repeat the same thing again and again… even over a long period of time. This might help us remember, but does it help us learn?
Research shows that learners who make a deliberate effort to improve are more likely to do so, even when they’re already quite experienced to begin with. This holds true across domains as diverse as sport, music, and others.
Ericsson (2014, p. 22) refers to one oft-cited study that illustrates this:
“Ericsson, Krampe, and Tesch-Römer (1993) collected detailed diaries of the daily activities of expert-level musicians who had studied music for over 10 years. They found reliable differences in the weekly amount of practice alone (deliberate practice), but not in the total amount of music-related activity (experience). The expert musicians with the higher levels of performance practiced alone for about 25 hours per week, three times more than the less accomplished expert musicians. For comparison, they found that amateur musicians of the same age practiced less than 2 hours per week, which is less than 10% of the amount for the best group of expert musicians.”
So simply ‘studying’ or ‘re-studying’ is not the same as deliberately practising. This kind of focused, intentional practice involves well-defined activities which are pitched appropriately for the learner (not over- or under-challenging), providing the opportunity to repeat, to spot mistakes and to be given useful corrective feedback. (More on this in just a moment!).
This means hard work – but hard work which should eventually pay dividends. Merely highlighting and rereading texts are not generally effective approaches to really developing expertise, for example. Learners need to be aware of the need to make a deliberate effort, to test their own knowledge, to reflect on why certain answers are correct / true, and to distribute their practice sessions over time (keep reading for more insight on this point in particular).
And for educators and learning designers, this means we should provide a wide variety of task types and complexity, revisit content frequently, give good feedback and use this feedback as a springboard for further instruction.
Spacing is usually better than cramming
Quick poll: Do you feel like you’re more likely to remember something if…
- …you study it intensively for a short period?
- …you keep coming back to it in short bursts over a longer period?
If you chose A, you might be surprised. Research suggests that many people do intuitively prefer ‘cramming’, thinking that an hour or two of intense study will help them more than 10 minutes per day. But this might actually be because many studies involve testing memory of new content almost immediately. In this case, it’s unsurprising that research participants are able to remember what they’ve just studied – after all, they’ve just studied it! But when we learn, we’re generally aiming for long-term, not short-term, recall ability.
Spacing vs. cramming for different types of knowledge
As we saw in Principle #2, there are different types of knowledge – declarative (what to do – facts, figures, dates, rules) and procedural (how to do it). For declarative knowledge, where the goal is essentially one of memorisation, spacing out study sessions is generally most effective – and when the learning task is particularly cognitively demanding, it’s a good idea to allow plenty of time for rest (and ideally, sleep) between study sessions. So distributing study over the course of several days is likely to be more helpful than spreading it out through just one day.
For more complex skills (such as proficiency in a new piece of software, learning to knead dough, using a handheld gaming device), we need to combine both declarative and procedural knowledge. In this case, spacing out practice may help at first for remembering the elements or steps involved; but to make a new procedure more automatic, a more experiential learning process (a.k.a. ‘learning by doing’) involving massed practice may be necessary.
Let’s consider a familiar example: learning to drive.
The spaced repetition element:
After hearing it many times over several days, you may be able to remember your driving instructor’s explanation of how to change gear…1. Release the accelerator pedal and, at the same time, press the clutch down.2. Move the gear stick gently but positively from one position to another.3. Release the clutch slowly and simultaneously press down on the accelerator.
The massed repetition element:
…but you won’t fully get the hang of this until you have to drive around an area with multiple turns and junctions, changing gear again and again within a short space of time!
Remember that during any study session, it’s very important to keep attention focused in order to make the most of their limited working memory capacity. This is influenced by many factors, including cognitive load, presence/absence of distractions, tiredness, and emotional state, so learners and learning designers need to optimise the learning environment with these factors in mind.
Remembering is better than being reminded
Few of us have fond memories of taking tests at school. But this is another area in which our intuitions may be wrong about the usefulness of testing our ability to recall what we’ve studied.
Especially in the case of declarative knowledge, repeated study (re-reading and highlighting, for example) might seem more effective than repeated testing in the short-term; but for long-term recall, research shows that testing is actually more effective – particularly when the stakes are low, as in a pop quiz or end-of-unit review, as opposed to a career-influencing high-stakes exam.
In a 2006 study, researchers Roediger and Karpicke gave three groups a short text to read and remember. But while the first group re-read the text several times before taking a final memory test a week later, the other two groups had at least one practice test before the final memory test.
The researchers found that “the positive effects of testing were dramatic”: students in Group 3 recalled 50% more after a week than students in Group 1, even though students in Group 3 read the passage only 3.4 times and those in Group 1 read it 14.2 times.
So the most effective way to move new information into your long-term memory is to frequently and actively try to remember it. Next time you’re struggling to remember something, give yourself a moment or two to really try before just googling it. You’ll be more likely to remember it more easily next time.
The study also revealed another interesting factor: if you test yourself under conditions that are similar to the ones in which you’ll be using your new knowledge or skills ‘for real’, then it may not actually be that important whether you even get feedback on your efforts.
Feedback is a crucial part of the learning process
We know that tests are useful tools for learning, even if you don’t get the answers right. But when we do this, getting corrective feedback is particularly important. And that’s especially true if the test you took wasn’t just one of several low-stakes recall opportunities spread throughout the learning process.
And what about the feedback learners get on their efforts? Is it enough to say “well done” or “wrong” and move on?
Certainly not. And even “try again” is only somewhat more helpful than saying “wrong” and giving the correct answer.
What makes good feedback?
Beliefs about the most effective kinds of feedback vary based on different theories of learning; but it’s generally agreed that effective feedback need to do more than just confirm or give the correct answer. It needs to be meaningful to learners, clearly explained and focusing their attention so they understand the gap in their knowledge and can then fill it.
Feedback needs to be detailed enough for the learner to monitor and evaluate their own knowledge, behaviour, strategies and progress. In this sense, good feedback overlaps with further instruction, by reshaping and developing what the learner already knows.
And of course, feedback can focus either on the task itself or on how the learner approached it. This could involve showing learners how to do a task more effectively next time, relating the feedback to their learning goals, and encouraging the learner to take more responsibility for their own learning success.
In short, research generally suggests that the nature of the learning process and what follows it is just as important as the ‘results’ that we’re often more interested in.
So when does ‘practice make permanent’?
- Focused and deliberate, not just repetitive;
- Spaced out over time (including some intensive practice sessions, depending on what’s being learned);
- The result of conscious efforts to remember by the learner, not spoon-feeding by instructors/materials;
- Followed up with prompt, clear, focused feedback.
Our third principle for designing an effective learning experience is all about providing enough ongoing practice of the right kind for whatever is being studied, along with constructive feedback on the learner’s efforts.
In an ideal world, everyone would have the necessary motivation, skills, support and self-awareness to direct their own learning effectively, as well as enough time and minimal pressure or other constraints. But in the real world, we often find ourselves needing to learn something fast because of some kind of pressure from external factors. Maybe an employer is starting a new initiative to upskill the workforce, or perhaps a change in life circumstances has suddenly prompted a need for new skills or qualifications.
For adult learners with plenty of other priorities and other demands on their time, as learning designers we have to be confident that the experiences we design will actually work.
Our hope is that these principles – which we’re regularly reviewing and updating as we learn more – can help anyone who is designing a learning experience to make more evidence-informed choices and therefore serve learners better.
If you’re interested, you can find out more about the work we do helping organisations to develop online learning here. And if you’d like to have a chat, we always love talking to people about creating learning experiences – just drop us a line.
Learning design principles whitepaper
If you’d like to go a bit deeper, then download our whitepaper. This breaks the three principles down into sub-principles, with ideas for how each can be applied. It also has more background on the research, and complete linked references if you’d to explore the original research that informed our principles.
This article is based on a series of blog posts written by Laura Patsko. Laura led the research and development of our learning design principles and wrote the Learning Design Principles whitepaper. Laura has worked with LearnJam on a number of projects recently. Laura is a freelance language and pedagogy consultant, maker, speaker, writer, walker and traveller – not necessarily in that order.
Agarwal, P. K., Bain, P. M., & Chamberlain, R. W. (2012). The value of applied research: Retrieval practice improves classroom learning and recommendations from a teacher, a principal, and a scientist. Educational Psychology Review, 24(3), 437–448.
BBC [Coughlan, S.] (2015). Computers ‘do not improve’ pupil results, says OECD. Retrieved 9 October 2019 from https://www.bbc.com/news/business-34174796
Bird, C. M., & Burgess, N. (2008). The hippocampus and memory: Insights from spatial processing. Nature Reviews Neuroscience, 9(3), 182–94. doi:10.1038/nrn2335
Bjork, E. L., & Bjork, R. A. (2011). ‘Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning.’ In M. A. Gernsbacher, R. W. Pew, L. M. Hough, J. R. Pomerantz (Eds.) & FABBS Foundation, Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64). New York, NY, US: Worth Publishers.
Breckwoldt, J., Ludwig, J. R., Plener, J., Schröder, T., Gruber, H. & Peters, H. (2016). Differences in procedural knowledge after a “spaced” and a “massed” version of an intensive course in emergency medicine, investigating a very short spacing interval. BMC Medical Education, 16 (Article 249). doi:10.1186/s12909-016-0770-6
Burns, A. (2016). ‘Teaching speaking: Towards a holistic approach’. Paper presented at 25th ETA-ROC Anniversary Conference: Epoch Making in English Language Teaching and Learning. Taipei, Taiwan. November 2016.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132, 354–380.
CESE [Centre for Education Statistics and Evaluation] (2017). Cognitive load theory: Research that teachers really need to understand. Published online by NSW Government [Australia] at https://www.cese.nsw.gov.au/images/stories/PDF/cognitive-load-theory-VR_AA3.pdf
Cheek, D.W. (2015). ‘A panoramic view of the future of learning and the role of design(ers) in such experiences.’ In B. Hokanson, G. Clinton & M. W. Tracey (Eds.), The design of learning experience: Creating the future of educational technology, London: Springer, pp. 5-37.
Chen, O., Castro-Alonso, J. C., Paas, F. & Sweller, J. (2018). ‘Undesirable difficulty effects in the learning of high-element interactivity materials’. Frontiers in Psychology, 9(1483), pp. 1–7.
Churchill, E., Okada, H., Nishino, T., & Atkinson, D. (2010). ‘Symbiotic gesture and the sociocognitive visibility of grammar in second language acquisition’. The Modern Language Journal, 94, p. 234–253.
Credé, M., Tynan, M. C. & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511. Accepted manuscript version retrieved 17 October 2019 from https://pdfs.semanticscholar.org/000a/51e0d37b8a557318559e19905d0f07f9ea00.pdf
Cripps, J. (2001/2). Dialing while driving: The battle over cell phone use on America’s roadways. Gonzaga Law Review 37 (2001/2002), pp. 89–119.
Davachi, L., Kiefer, T., Rock, D., & Rock, L. (2010). Learning that lasts through AGES: Maximizing the effectiveness of learning initiatives. NeuroLeadership Journal, 3.
Davis, J., Balda, M., Rock, D., McGinniss, P. and Davachi, L. (2014). The science of making learning stick: An update to the AGES model. NeuroLeadership Journal, 5.
Deci, E. L. & Ryan, R. M. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
Dörnyei, Z. (2009). ‘The L2 motivational self system.’ In Dörnyei, Z. & Ushioda, E. (eds). Motivation, language identity and the L2 self, pp. 9–42. Bristol: Multilingual Matters. Retrieved 16 October 2019 from https://pdfs.semanticscholar.org/f4ee/5fae81ae33942821e4d52ee5124c3f2b0c91.pdf
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J. & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58.
Ericsson, K. A., Krampe, R. T., & Tesch- Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
Ericsson, K. A. (ed). (2014). The road to excellence: The acquisition of expert performance in the arts and sciences, sports, and games. New York, NY: Psychology Press.
Forehand, M. (2005). Bloom’s Taxonomy: Original and revised. In M. Orey (ed.), Emerging perspectives on learning, teaching, and technology. Retrieved 9 October 2019 from https://cdn.vanderbilt.edu/vu-wp0/wp-content/uploads/sites/59/2010/06/12092513/BloomsTaxonomy-mary-forehand.pdf
Gartner (2019). Supply chain brief: Amazon’s plan to retrain 100,000 employees puts new emphasis on building internal skill sets. Retrieved 22 October 2019 from https://www.gartner.com/en/documents/3955982.
Goh, C. C. M. & Burns, A. (2012). Teaching speaking: A holistic approach. Cambridge: Cambridge University Press.
Hartwig, M., & Dunlosky, J. (2012). Study strategies of college students: Are self- testing and scheduling related to achievement? Psychonomic bulletin & review, 19, (1), 126–134.
Hase, S. & Kenyon, C. (2000). From androgogy to heutagogy. Published online by ultiBASE at http://ultibase.rmit.edu.au/Articles/dec00/hase2.htm
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Hays, M. J., Kornell, N., & Bjork, R. A. (2013). When and why a failed test potentiates the effectiveness of subsequent study. Journal of Experimental Psychology: Learning, Memory, and Cognition, Advance online publication. doi:10.1037/a0028468
Immordino-Yang, M. H. and Damasio, A. (2007). ‘We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education.’ Mind, Brain, and Education, 1: 3–10. doi:10.1111/j.1751-228X.2007.00004.x
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
Kirschner, P. A. & Neelen, M. (2019). What we already know determines what, how, and how well we learn. Retrieved 22 May 2019 from https://3starlearningexperiences.wordpress.com/2019/05/21/what-we-already-know-determines-what-how-and-how-well-we-learn/
Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories: Is spacing the “enemy of induction”? Psychological Science, 19(6), 585–592. doi:10.1111/j.1467-9280.2008.02127.x
Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23, (9), 1297–1317.
Kornell, N., Hays, M., & Bjork, R. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, (4), 989.
Kornell, N., Castel, A. D., Eich, T. S. & Bjork, R. A. (2010). Spacing as the friend of both memory and induction in young and older adults. Psychology and Aging, 25(2), 498–503.
Kornell, N., Bjork, R. A. & Garcia, M. A. (2011). Why tests appear to prevent forgetting: A distribution-based bifurcation model. Journal of Memory and Language, 65, 85–97.
Kornell, N., Klein, P. J., & Rawson, K. A. (2014). Retrieval attempts enhance learning, but retrieval success (versus failure) does not matter. Journal of Experimental Psychology: Learning, Memory, and Cognition, Advance online publication. doi:10.1037/a0037850
Kounios, J., & Beeman, M. (2009). The Aha! moment: The cognitive neuroscience of insight. Current Directions in Psychological Science, 18(4), pp. 210–216. doi:10.1111/j.1467-8721.2009.01638.x
Lakshmanan, A., Lindsey, C. D. & Krishnan, H. S. (2010). Practice makes perfect? When does massed learning improve product usage proficiency? Journal of Consumer Research, 37, 599–613. doi: 10.1086/655686
Lantolf, J. P. & Thorne, S. L. (2006). Sociocultural theory and the genesis of second language development. Oxford: Oxford University Press.
Lehmann, A. C. (1997). The acquisition of expertise in music: Efficiency of deliberate practice as a moderating variable in accounting for sub-expert performance. Perception and cognition of music, 161–187.
Leroy, V., Grégoire, J., Magen, E., Gross, J. J. & Mikolajczak, M. (2012). Resisting the sirens of temptation while studying: Using reappraisal to increase focus, enthusiasm, and performance. Learning and Individual Differences, 22, 263–268.
Lieberman, M. D., Inagaki, T., Tabibnia, G., & Crockett, M. J. (2011). Subjective responses to emotional stimuli during labeling, reappraisal, and distraction. Emotion, 11(3), 468–480.
Maclachlan, K. (2004). ‘We can giggle about being thick together’: utilising the social dimensions of learning in the workplace. Research in Post-Compulsory Education, 9(3), 337–352.
McCabe, J. (2011). Metacognitive awareness of learning strategies in undergraduates. Memory & cognition, 39(3), 462–476.
McEvoy, S. P., Stevenson, M. R., McCartt, A. T., Woodward, M., Haworth, C., Palamara, P. & Cercarelli, R. (2005). Role of mobile phones in motor vehicle crashes resulting in hospital attendance: a case-crossover study. BMJ, 331:428. doi:10.1136/bmj.38537.397512.55
Murray, G. (2014). The social dimensions of learner autonomy and self-regulated learning. Studies in Self-Access Learning Journal, 5(4), 320–341.
Murre, J. M. J. & Dros, J. (2015). Replication and analysis of Ebbinghaus’ Forgetting Curve, PLOS ONE (10)7. doi:10.1371/journal.pone.0120644
Neelen, M. & Kirschner, P. (2018). Why and how to use worked examples in the workplace. Retrieved 14 June 2019 from https://3starlearningexperiences.wordpress.com/2018/11/13/why-and-how-to-use-worked-examples-in-the-workplace/
Philp, J. (2017). What do successful language learners and their teachers do? : Part of the Cambridge Papers in ELT series. [pdf] Published online by Cambridge University Press.
Reinders, H. & White, C. (2016). 20 years of autonomy and technology: How far have we come and where to next? Language Learning & Technology, 20(2), 143–154. http://dx.doi.org/10125/44466
Roediger, H.L., & Karpicke, J.D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17, 249–255.
Schweppe, J. & Rummer, R. (2014). Attention, working memory, and long-term memory in multimedia learning: An integrated perspective based on process models of working memory. Educational Psychology Review, 26(2), 285–306.
Sheard, M. (2009). Hardiness commitment, gender, and age differentiate university academic performance. British Journal of Educational Psychology, 79, 189–204.
Simonsmeier, B. A., Flaig, M., Deiglmayr, A., Schalk, L. & Schneider, M. (2018). Domain-specific prior knowledge and learning: A meta-analysis [manuscript in progress]. Retrieved 22 May 2019 from https://www.uni-trier.de/fileadmin/fb1/prof/PSY/PAE/Team/Simonsmeier/SimonsmeierEtAl2019.pdf
Stein, M. K., & Smith, M. S. (1998). Mathematical tasks as a framework for reflection: From research to practice. Mathematics Teaching in the Middle School, 3(4), 268–275.
Thurlings, M., Vermeulen, M., Bastiaens, T., & Stijnen, S. (2013). Understanding feedback: A learning theory perspective. Educational Research Review, 9, 1–15.
Ushioda, E., & Dörnyei, Z. (2009). Motivation, language identities and the L2 self: A theoretical overview. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, language identity and the L2 self (pp. 1–8). Bristol: Multilingual Matters.
Van den Bossche, P., Gijselaers, W. H., Segers, M., & Kirschner, P. A. (2006). Social and cognitive factors driving teamwork in collaborative learning environments: Team learning beliefs and behaviors. Small Group Research, 37(5), 490–521.
Veltre, M. T., Cho, K. W. & Neely, J. H. (2015). Transfer-appropriate processing in the testing effect, Memory, 23:8, 1229–1237. doi: 10.1080/09658211.2014.970196
Verkoeijen, P. P. J. L. & Bouwmeester, S. (2014). Is spacing really the “friend” of induction? Frontiers in Psychology, 5 (259), 1–8. doi:10.3389/fpsyg.2014.00259
Ward, P., Hodges, N. J., Williams, A. M., & Starkes, J. L. (2004). ‘11 Deliberate practice and expert performance’, in Skill acquisition in sport: Research, theory and practice, 231, pp. 434–479.
World Economic Forum (2018). The future of jobs report 2018. Retrieved 22 October 2019 from http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf
Zimmer, H. & Fu, X. (2011). The influence of expertise and of physical complexity on visual short-term memory consolidation. The Quarterly Journal of Experimental Psychology, 64:4, 707-729, doi:10.1080/17470218.2010.511238.