The Science Behind Learning
The German psychologist Hermann Ebbinghaus was the first to hypothesize and study (in 1885) the now-famous forgetting curve – often referred to as Ebbinghaus' Forgetting Curve. He noticed that at the time of learning, or taking in new information, a learner "knows" 100% of the material. However, the memory of what one learned starts to diminish immediately, and rather rapidly.2 His studies have been replicated much more recently and have come to the same conclusion – memory loss is exponential immediately after a learning event. As you get further away from the learning event in time, more and more of the information you learned is lost.2 Thankfully, it is not as bleak as it sounds. Ebbinghaus and subsequent researchers have seen that there is a way to combat the natural forgetting curve. Repetition over time and being reexposed to the same material are 2 key strategies that can combat the forgetting curve.3 Repetition of the material at spaced intervals after the learning event changes the trajectory of the curve. At each intervention or repetition, the curve becomes less steep, and the learning is more engrained.3 The "Baby Shark" tune!
Ebbinghaus also discovered a concept called "overlearning." Essentially, if you practice something more than is required to learn it, the information is stored much more strongly, and the effects of the forgetting curve for this information is a much shallower slope; for example, if you have kids, the fact that you could sing "Baby Shark" in your sleep may be a product of overlearning. You were not exposed to it just once, twice, or three times – you probably have heard that song far more than was required to learn it, and I would venture to guess that if asked 10 years from now, you would still be able to recall it.
Not all forgetting curves are the same. Many factors influence the rate of forgetting. The meaningfulness of the information to the learners, how it's represented, and the learners' physiological state (stress levels, sleep pattern, hunger, etc.) impact a forgetting curve greatly. Essentially, what you are remembering and how it is presented to you matters. For instance, if you burn yourself on a wood stove, you probably do not need to touch the stove again the next time because you've forgotten that lesson. It was intense, and you don't need reminding. Ebbinghaus discussed the intensity of our emotions and our attention as the 2 factors that determine how steep the forgetting curve is, however, to ensure we stay focused on the objective of this paper, let's keep the discussion on these factors aside.4 The Ebbinghaus' Forgetting Curve makes a lot of sense when we look at the way our brains learn. When we learn something new, connections are created between neurons in the brain. The more you repeat this learning, the stronger these connections become, making them faster and more efficient. It is similar to walking the same path through dense woods – the first time, it's slow going; as you continue to tread the same path, the path becomes clearer and you can navigate the same path much faster. New learning literally reshapes and rewires your brain, a phenomenon called neuroplasticity.5 When you ask your brain to retrieve the new information, you solidify the connections between the neurons more and more each time, and the information moves from short-term memory (working memory) to long-term memory.6 ■
Sensory inputs enter your sensory memory, but last less than a second. If you do not act, the memory is gone.
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Act on the information to move it into your working memory.
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Retrieve what you already know and work on the new information to associate it with existing knowledge. This will encode new memories, which means you have learned the material.
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You have now learned the material by storing it in your long-term memory. it is available to you the next time you need to learn new material.
In short, you need to be exposed to the learned information a few times, and ask your brain to retrieve the information to move it from short-term memory to long term memory. Long term memory is where we need it to be so that a learner can access the learned information in the flow of work.
DIKW Pyramid
The DIKW Pyramid is a widely accepted model in knowledge management. The pyramid shows how Data (components), Information (processed data into something that is meaningful), and Knowledge (a skill or larger piece of information within a system) are connected to Wisdom, where we add context, judgment, and the ability to see more broadly than the other layers.
In the first cell, we can see bits of uncategorized things, which aren't very useful. They have no context, and no larger meaning (e.g., random letters). The next cell indicates adding a bit of time and context to the data, and we see that we're able to categorize the data to some extent and it becomes meaningful (e.g., words). Again, as we add context and awareness, we start to see paths and the interconnectivity of the components – and we now have knowledge (e.g., sentences, paragraphs, or a book). For your learners, this would be the ability to pass a test, indicating that they've gained the knowledge you were imparting. They understand the data, and how it fits together. Very often though, that's not good enough for our learners, and we need them to be able to be more strategic to take on the learning in a more holistic manner and do something differently than they did before. For that, we need to add insight and behavior change. In this model, the next level is insight. In this stage, our learners have that "ah-ha" moment and understand what the data should mean to them in their larger schema. They understand how what they have learned could apply more broadly to other areas or draw conclusions about the learned material that was not given to them directly.
The last cell shows behavior change. We see that insight gained is leading to the learner doing something different because of the new information. As learning professionals, this is where we are hoping all our learning leads – a new or adjusted behavior.
The way we move up the pyramid (or across the cells) is by increasing exposure to the content and adding context. We can make connections between new information more easily when we can scaffold it to existing information.
It would be remiss to not discuss cognitive load when we are thinking about learning retention. Cognitive load theory came from the understanding that working memory has limited capacity, and therefore when asking a learner to take on new information, we need to be aware of the limitations and work with them instead of against them.
Cognitive load types can be broken down into 3 basic types:
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Intrinsic cognitive load: Complexity of the information. This is at the component level of the learning task – how complex is the task. The intrinsic cognitive load for arithmetic is less than the that of complex calculus. Our goal is to simplify the intrinsic cognitive load.
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Extraneous cognitive load: Noise around the information. This could be anything from poor instructional design to excess information that the learner does not need to distracting background noise. Our goal should be to reduce this load.
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Germane cognitive load: Process of adding the new information into existing schemas – effective learning. This is peak performance for learning, where the new information is laddered in and enmeshed with existing knowledge. We want to maximize this.