instructional design: Working memory & expertise reversal

Working memory limitations indicate that we should design learning in such a way as to reduce cognitive load. Working memory is a resource with limited capacity and duration – there is only so much we can attend to at any one time. This is not just a proposal of working memory theory – it’s also a real-world truth that’s easy to test (just try silently reading a paragraph of a book while simultaneously reciting la-la-la out load. The chances are you’ll barely be able to recall what you’ve read). This capacity is often referred to as load and has been encapsulated into a model of instructional design known as Cognitive Load Theory (CLT).

According to CLT, load comes in three versions. Intrinsic load concerns the demands of the task in hand, such as comprehending the task, the establishment of connections between interacting elements of information. Extraneous load is described as task unrelated activities that still require the use of cognitive resources. Using the above example, intrinsic load is the reading of the paragraph and the understanding of the task involved; repeating la-la-la is extraneous load, it is unrelated to the reading but still takes up much needed cognitive resources and might negatively impact on our understanding of the passage we are reading. The third type of load (germane) is related to the creation and execution of schemas.

Intrinsic and extraneous load are effectively competing for limited resources during the learning activity. It, therefore, makes logical sense to limit task-unrelated material, so effective instructional design should restrict sources of extraneous load so that resources are available to maximise intrinsic load.  

Knowledge built up over time and used to form cognitive schemas can make later learning easier and more efficient. Current knowledge informs later learning so, for example, knowing the ins and outs of the Treaty of Versailles can then help us understand the causes of the Second World War by providing context, and context aids deeper understanding; understanding how to complete a particular mathematical equation helps to solve others (and being able to recall your times tables makes mental arithmetic so much easier and less stressful).

Similarly, the skills gained in learning to ride a bike can then be utilised when we wish to ride a motorcycle. Current learning is built upon previous learning (the more we know, the greater our capacity to learn more).

But what if you’re learning something for the first time?

Load Reduction Instruction (LRI) advocates the use of direct teaching methods in the early stages of learning something new, but gradually introduces more independent learning methods. LRI suggests five distinct stages.

1. Difficulty reduction

2. Support and scaffolding

3. Practice

4. Feedback

5. Guided independence

The early stages of learning are dominated by direct instruction but, gradually, this encourages the learner to become more independent. The direct instruction component allows for new schemas to develop so that, as tasks become more complex, information in long-term memory can be more effectively utilised.  As students progress from task novice to task expert, the way the information is presented can be adapted; the scaffolding can gradually be taken away and more independent teaching methods used. If, however, the teacher or trainer continues to use the same methods for novice learners and expert learners, these instructional methods become ineffective for those with a wider knowledge base. This is when we see what is known as the Expertise Reversal Effect.

Learning, in this instance, can be viewed as the active reconstruction of relevant schemas; as new information is acquired, knowledge schemas are adapted and updated, resulting in more advanced knowledge and skills. In addition, this available schematic knowledge influences higher -level cognitive performance by generating problem-solving strategies.

According to Kalyuga, the Expertise Reversal Effect occurs when ‘the relative effectiveness of the different learning conditions reverses with changes in the level of learner expertise’ (expertise, in this context, refers to the narrow, task-specific expertise). Learners with more knowledge in a specific area, therefore, aren’t going to benefit from the methods we might use for those learners with little or no knowledge. 

This is because information that novice learners might need is processed individually, while more expert learners can combine this information into a single entity, that is, a schema. Let’s take a task such as turning left at a junction while learning to drive a car. The novice driver thinks in terms of individual tasks; look in the rear-view mirror, signal the intention to turn by operating the appropriate indicator and, finally, manoeuvring the vehicle. Experienced drivers rarely think of this task in this way; they access the ‘turning left’ schema, and the schema activates the behaviour.

Thinking about how we learn, everything from playing a musical instrument to driving a car (or, indeed, preparing for a formal exam), can help us better understand how to teach others. Learning anything would be impossible without the cognitive architecture that allows us to recall the relevant information, but as both research and personal experience find, we are limited in our ability to retain and recall this information. Recognising these limitations provides the opportunity to design methods for ourselves and others that may make learning more reliable, efficient and less stressful.

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