When designing a sequence of learning there are a few things to consider before you decide how inherently complex or difficult your content can be. In this post I wish to focus on a concept from cognitive load theory described as intrinsic load.

Intrinsic load is characterised by the complexity of the thing to be learned. It’s the load that is inherent to the task and is inescapable if we want the task to be achieved. It is what we ideally want our students’ attention to be entirely focused on. It is what consumes the working memory (WM). However, how high or demanding this load can be is very much affected by the level of existing knowledge the learner possesses that is connected to the new load being processed.

The existing knowledge is stored in the long-term memory (LTM), and if it is able to be retrieved to assist in the processing of the new information, then the intrinsic load of the task is reduced. It’s like having someone come along and help you lift some heavy weights – it’s easier when they help. When the LTM can assist, the inherent complexity of the task can be increased.

Let’s imagine a task that inherently has 5 or 6 components that make up its complexity. To assist in the processing of the complexity the WM will search the LTM to see if there are any relevant connections between what exists and the new learning. If it can’t make any connections, then the WM is on its own, and because of its limited capacity, will not be able to process all of the components of the new context. However, if the search is fruitful, then the complexity of the 5 or 6 components can be mitigated against.

Take the learning of this maths problem as a good example. The problem, find the value of x if 3x + 3 = 12 involves more than 5 components of knowledge to solve: algebraic notation, balancing equations, order of operations, multiplication, division, addition, subtraction. It is clear that if you gave such a problem to someone with absolutely no mathematical knowledge the problem would be impossible. However, for the student whose WM is able to successfully search the LTM for understanding of addition, subtraction, multiplication and division and even order of operations, the task’s intrinsic load is now essentially only related to algebraic notation and to the balancing of equations. The other components don’t take up valuable WM real-estate – they automatically are recalled to assist in processing the unknown components. As a result, the task is made significantly easier, relatively.

Relatively is the key word here. As the student accommodates this new learning into their LTM, then the complexity of the next task can be increased. As Ericsson and Kintsch found, the limitations of the WM effectively disappear when the LTM can be used to support intrinsic load.

This has large implications for how learning sequences are designed. Understanding what level of prior knowledge your students have will determine the starting position of your sequence. But also, understanding how many steps of complexity away your students are from a desired learning goal will assist in deciding what pedagogy is relevant to helping them get there. If the relative complexity is too high and the intrinsic cognitive load of the task will overwhelm the WM, an inefficient learning context will result. If the relative complexity of the new task is manageable within the WM, then you have set the right amount of challenge.

**References**

Ericsson, K. , Kintsch, W. & (1995). Long-Term Working Memory. *Psychological Review, 102 *(2), 211-245.

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I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

A key problem when teaching History is the high level of content. I feel as if I hammer students with information, thereby battering “working memory”. It seems a constant challenge on courses such as A-level and GCSE.

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Hi Dave. Yes, the high level of content is an issue, but that makes it even more important that the learning sequence is designed so clear connections are made as often as possible between content. The time it takes to process new content will be less as the WM is supported more in the new learning.

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