Cognitive load theory is a theory of learning derived from cognitive science that has immediate practical implications for teachers. It also provides a theoretical underpinning for a model of explicit teaching that has emerged from a range of classroom studies from the 1960s onwards. Understanding cognitive load theory helps teachers design teaching materials and lessons that reduce the demands on learners’ working memory, so that they learn more effectively.

In his webinar, Dr Greg Ashman provided an overview of the key ideas of cognitive load theory and its main practical implications, and presented a model of explicit teaching that is consistent with the theory.

Cognitive load theory developed from experiments that John Sweller carried out in the 1980s to do with problem solving. We all have an in-built problem-solving strategy called means-ends analysis, which involves defining a goal, trying out a problem-solving move, and evaluating how close that gets you to your goal. If the strategy gets you closer to your goal, you keep it, but if it gets you further away, you reverse it. Sweller observed that this approach is extremely mentally taxing and leaves little processing power to notice other things, such as patterns, meaning that while means-end analysis can make students very effective at solving problems or completing investigation, they may well not learn the key concepts that the problem or investigation was designed to teach. He was interested in finding out why and came up with cognitive load theory to explain the most effective ways for students to learn.

There are two categories of knowledge, biologically primary and biologically secondary knowledge. Biologically primary knowledge refers to knowledge that we have evolved to acquire effortlessly (such as learning to speak in our native language), while biologically secondary knowledge describes knowledge that our brains are not designed to acquire effortlessly. For example, until the nineteenth century, reading and writing were the preserve of only a very small group of people (mostly clerics and priests), and not enough time has passed since the development of reading and writing for human evolution to act on it, even if there is an evolutionary advantage to being literate. This means that our brains are not designed to learn to read and write naturally, the way we learn to speak. The purpose of school is to teach this biologically secondary knowledge, which is culturally relevant but effortful to acquire because we have not evolved to acquire it. Importantly, cognitive load theory only applies to biologically secondary knowledge.

Cognitive load theory proposes a model of the mind as consisting of two main components, working memory and long-term memory. Long-term memory is effectively limitless, while working memory is extremely limited. Working memory roughly correlates to a person’s conscious thoughts in the moment. The limit of working memory is roughly seven individual items (such as seven random digits), and this capacity reduces to roughly four once you start working with or processing those items. When we acquire new information, it must be processed through working memory, but once it has been encoded in long-term memory, our ability to use it is limitless. In long-term memory, ideas are stored in connected webs (called schema) and are linked semantically according to their meaning, making us able to draw a large amount of information from our long-term memory into our working memory as one unit. Note that this is a model, not a neuroscientific account of how memory works.

Simple cognitive load theory effects refer to the way that novices learn new and complex material. They include the worked-example effect, which shows that students learn more effectively through a combination of fully worked examples and problems, rather than being left to solve a problem through a process of trial and error. Another example is the split-attention effect, such as when learners have to use a key to identify different elements of a diagram. In fact, they learn more effectively when the labels of the different parts of the diagram are integrated into the diagram. A third example is the redundancy effect, such as when teachers and presenters read aloud information that is also presented visually. This means that students are trying to process information by reading it and listening to it at the same time, which causes cognitive overload.

Element interactivity describes the extent to which the different elements of the material being learned interact with each other, and depends on the levels of knowledge of the learner. For example, memorising the periodic table of elements has low element interactivity because each item is separate, therefore the cognitive load of learning each item is low. Learning how to balance chemical equations or complete maths equations involves much higher element interactivity, as all the different components of the equation interact with each other, and therefore the cognitive load is much higher for learners without well-developed schemas for completing these operations. In writing, a task that involves checking for capital letters at the start of each paragraph has low element interactivity, while writing a paragraph has much higher element interactivity as all the components of the paragraph (sentence structure, spelling, content, and so on) depend on and interact with each other. A key way to reduce the cognitive load for novice learners is to start with simpler equations and tasks, allowing schema to be formed, and gradually increase the difficulty.

Complex cognitive load theory effects apply to non-novice learners learning relatively complex academic material. One of these is the expertise-reversal effect, which states that an expert learner with a well-developed schema for solving particular types of problems will learn more from practising solving problems than from looking at worked examples. In practice, using the expertise-reversal effect involves gradually releasing control to the learner as they move from being a novice to being more expert in that particular area, using explicit teaching in the early stages and gradually moving the student towards problem solving as their schema become well-established in long-term memory.

The ‘curse of knowledge’ describes the way that it can be difficult to understand the effort of learning something which has become effortless for us because we have well-developed schema in our long-term memories. Often teachers and professors may find it difficult to relate to the challenges of learning new information and procedures because they have so effectively coded this knowledge in their long-term memories that retrieving and using it is effortless. They may fail to recognise that something they can do in one straightforward step (such as solving 3x = 18) actually involves a number of separate elements and steps for novice learners.

Explicit teaching is known by many different names, such as direct instruction and active learning (although some of these terms are more accurate than others).The form of explicit teaching that is backed by research involves a whole system of teaching, rather than one-off moments of just-in-time explicit explanation. One of the best systems of explicit teaching (although he does not use this term) is Barack Rosenshine’s Principles of Instruction, which aligns with the gradual release of control described by cognitive load theory’s expertise-reversal effect. The defining feature of explicit teaching is that, for novice learners, new concepts are fully explained and procedures are fully modelled before students are asked to apply or use those concepts and procedures.

One of the most valuable things that teachers can do is focus on achieving a high success rate, as per Rosenshine’s principles. If the students are consistently getting around 80% on tasks, you are ensuring a high success rate and are likely effectively managing students’ cognitive load. One way to get immediate feedback on students’ levels of understanding is to use mini-whiteboards, allowing you to quickly check their responses.

You can read more of Greg’s work here.

You can read Rosenshine’s principles here.

Here is a link to the presentation on explicit maths teaching at Clarendon College that Greg mentions during the presentation.