Written by Dr Ouhao Chen, Lecturer in Educational Psychology and Mathematical Cognition at the Department of Mathematics Education, Loughborough University. The article is edited by Bethany Woollacott.

This post is based on Ouhao’s recent publication with Fred Paas and John Sweller. The paper is open access and linked at the end of this blogpost.

Introduction

Measuring the complexity of a task is an on-going topic. Researchers from different domains have proposed various approaches to measure the complexity of tasks; however, all approaches are outside of the domain of education and do not consider the role of learners’ expertise in a specific domain. Cognitive Load Theory (CLT) suggests using the concept of element interactivity to measure the complexity of tasks in education.

What is ‘element interactivity’?

Element interactivity within CLT shows the degree of interconnectedness of elements within a learning task. An element could be a concept, a fact, or a procedure. The more elements in a learning task that are strongly interconnected, the higher the level of element interactivity of that task.

For example, memorising numbers 1, 2, 3 may have a low level of element interactivity because when you memorise 1, you can memorise it without referring to the other two numbers, therefore, the numbers can be memorised individually and separately. Comparatively, solving the equation 5x + 6 = 9 has a higher level of element interactivity because you must understand the equation in order to solve it, so you must process all the elements (5, x, +, 6, =, 9) simultaneously rather than individually.

Element Interactivity means difficulty?

It is important to clarify that a learning task could be very difficult but low in element interactivity. For example, memorising numbers could be a difficult task (if there are an unlimited amount of numbers), but is always low in element interactivity (as you could memorise the numbers individually and separately). However, once the elements of a learning task are interconnected, although the task might have only 2 or 3 elements, it could be very difficult. For example, solving the equation x + 3 = 6 has high element interactivity (5 elements interconnected for learning). Therefore, element interactivity and difficulty are associated but very different concepts.

Element interactivity is decided by learners’ expertise

When calculating the element interactivity to measure the complexity of a learning task, learners’ expertise plays an important role. This is because a learning task for novices may be high in element interactivity but low in element interactivity for experts, due to experts having more schema (chunks of individual elements). For example, solving the equation x + 3 = 6 has five interconnected elements for learning when the learners are respective novices; however, for relative experts, there would only be one element because they can retrieve the schema (i.e., chunks of knowledge) as an entity to be processed in working memory. Therefore, the level of learners’ expertise decides the level of element interactivity in a task, namely, the more knowledgeable you are, the lower the level of element interactivity in a task.

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“The more knowledgeable you are, the lower the level of element interactivity in a task.“

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Implications

The key difference between using the element interactivity approach to measure task complexity rather than traditional approaches is that the element interactivity approach considers the knowledge levels of learners. Therefore, the element interactivity approach has some important practical implications for education.

In particular, when teaching relative novices, if the level of element interactivity is very high then this consumes lots of working memory resources (high intrinsic load); therefore, teachers should design their instructions to reduce extraneous load and make sure the learning does not overload working memory capacity. Comparatively, when teaching relative experts, the level of element interactivity of the same task would be lower so the instructional design might not be so important. For example, you could ask relative experts to solve the equation of 5x + 6 = 9 without any instructions, because they could solve this equation by retrieving schema (processing one element, imposing very low intrinsic cognitive load). As such, engaging in problem solving without any instructions would still be manageable for relative experts. In comparison, asking novices to solve this problem without any instructions could impose heavy extraneous cognitive load (so worked examples could be designed for their learning).

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