the ITT crowd

musings from an initial teacher training provider

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Load Reduction Instruction

Load reduction instruction (LRI) is an umbrella term coined by Andrew Martin referring to instructional approaches that seek to reduce and/or manage cognitive load in order to optimise students’ learning and achievement. There is significant overlap with Cognitive Load Theory.

LRI encompasses explicit and direct instruction. At the appropriate point in learning, LRI also involves less structured approaches such as guided discovery-based, problem-based, and inquiry-based learning.

A major tenet of LRI is that students are at first novices with respect to academic skill and subject matter and that a structured and somewhat directional approach to instruction that reduces cognitive load is important for learning and achievement in the early stages of learning.

Then, as core skill, knowledge, fluency and automaticity develop, LRI emphasises the centrality of guided discovery-based, problem-based, and inquiry-based learning.

LRI is based on five principles at key points in the learning process: 1. Reducing the difficulty of a task during initial learning 2. Instructional support and scaffolding through the task 3. Ample structured practice 4. Appropriate provision of instructional feedback 5. Independent practice, supported autonomy, and guided discovery learning

Although numerous frameworks have recognised the roles of explicit or discovery approaches, LRI is distinct in that its emphasis is on reducing or managing the cognitive load on students as they learn and that this can comprise both explicit and discovery approaches.

LRI is thus termed, framed and developed deliberately to indicate why we engage its various instructional elements: namely, to deliver instruction and instructional support so as to appropriately reduce or manage the cognitive burden on the learner.

Essentially, LRI helps build the content of long-term memory and develops a level of fluency and automaticity that frees up working memory to apply to a given task or problem.

Importantly, fluency and automaticity also have implications for students’ motivation and engagement.

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Questions: Cognitive Load Theory

How do I know whether my learners are novices or experts?

How will I adapt my teaching to their learning stage?

How can I manage the complexity of learning required for this sequence of lessons?

How can I maximise the transition from working to long-term memory for all learners?

How can I minimise extraneous load through instructional design?

Once I have addressed these, what has been the impact on progress and learning?

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Conspectus: Cognitive Load Theory

In the past week, I have explored some of the key aspects of cognitive load theory. Here is a brief summary, or conspectus, of these posts.

Cognitive load theory provides principles and recommendations to support more effective learning through instructional design. These recommendations aim to transfer knowledge from working memory to long-term memory in the form of schemas. In addition, they aim to minimise the risk of overloading the working memory. Specifically, in relation to three key types of cognitive load, they aim to:

- Manage intrinsic load, ie the complexity of material - Maximise germane load, ie the transfer of material from working to long-term memory - Minimise extraneous load, ie the material is presented in a way that does not duplicate, clutter or confuse

We need to be aware of the differences between novice and expert learners, and how to move all learners towards expert learners, using examples of effective classroom practice such as:

- Worked example effect - Expertise reversal effect - Redundancy effect - Split attention effect - Modality effect

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Features of novice and expert learning

Cognitive load theory focuses on strategies to move learning from novice to expert. David Didau has created a list of some key features, whilst the Education New South Wales report on cognitive load theory includes some recommendations for the classroom, discussed in a previous entry.

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Novices and Experts

David Didau writes about the novice to expert model of learning in his blog ‘The Learning Spy’. One of the best understood principles of cognitive psychology is that novices learn and think differently to experts. These labels are domain-specific, not person-specific: a university professor may be a leading light in their academic field, yet not be able to boil an egg. We’re all novices at something, and many of us will be experts in at least one domain.

If we use the same approach to teaching for both experts and novices, we risk cognitive overload for the novice, or the expertise reversal effect for the expert.

Didau suggests two main hallmarks of expertise: 1. Automaticity of foundational procedures 2. Ability to see ‘deep’ structure within domains of expertise We need to master various procedural knowledge to the point where we no longer have to think about it so that it doesn’t take up space in working memory. These automatised procedures are often so well embedded that experts are not even aware of them. Although we might not actively think about these things, we most certainly think with them. 

Automaticity of foundational procedures In his book ‘How I wish I’d taught Maths’, Craig Barton uses two example questions to demonstrate how novices and experts may approach solving them. The first, find 25% of £300, shows how experts and novices could gain the correct answer, but that novices would find it cognitively more demanding. The second, a question from AQA 2017 GCSE Maths Higher Paper 1, is unlikely to be solved instantly even by the expert:

However, experts do not need to expend effort contemplating what each term in the formula means, how to square numbers, how to find a third, or what to do with the pi symbol. This frees up cognitive capacity to attend to more global features of the problem, such as correctly interpreting the question and devising a strategy to get to the solution. Experts are not only more likely to solve the problem, they are more likely to learn from the experience. Novices, however, are likely to get bogged down in the minutiae of the problem, experience cognitive overload and potentially not learn anything transferable.

Ability to see ‘deep’ structure within domains of expertise This comes with the experience of thinking about domain-specific knowledge. The more we know and the more practice we have at identifying and solving problems within in a domain, the more likely we are to see through the superficial trappings of a problem to the underlying structures beneath. When we become aware of these structures we become increasingly able to transfer our ideas between contexts.

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Cognitive Load: recommendations for the classroom

Cognitive load theory has produced a number of recommendations regarding instructional techniques that are directly transferable to the classroom. A selection of these are described in the Cognitive Load Theory report from the Centre for Education, Statistics and Evaluation, Education New South Wales, 2017). They illustrate how evidence-based cognitive load research can be used by teachers to improve student outcomes.

 The ‘worked example effect’  A ‘worked example’ is a problem that has already been solved for the learner, with every step fully explained and clearly shown. The ‘worked example effect’ is the widely replicated finding that novice learners who are given worked examples to study perform better on subsequent tests than learners who are required to solve the equivalent problems themselves.

The reason for this, according to cognitive load theory, is that unguided problem-solving places a heavy burden on working memory, inhibiting the ability of the learner to transfer the information into their long-term memory. The learner may effectively solve the problem, but because their working memory was overloaded they may not recognise and remember the rule that would allow them to quickly solve the same problem again in the future. 

The ‘expertise reversal effect’ The ‘expertise reversal effect’ is an important exception to the worked example effect. According to the expertise reversal effect, the heavy use of worked examples becomes less and less effective as learners’ expertise increases, eventually becoming redundant or even counter-productive to learning outcomes. 

This means that some instructional procedures such as worked examples, which assist learning for novices because they reduce cognitive load, are not effective for teaching more expert learners. While cognitive load theory supports fully guided instruction for novice learners, it also supports the gradual incorporation of more independent problem-solving tasks as learners gain expertise. 

The ‘redundancy effect’ Students do not learn effectively when their limited working memory is directed to unnecessary or redundant information. The ‘redundancy effect’ occurs when learners are presented with additional information that is not directly relevant to learning, or with the same information in multiple forms. An example is a textbook which includes both text and a diagram that needlessly repeat information, or a PowerPoint presentation in which the presenter reads the text presented on the screen. Requiring learners to process redundant information inhibits learning because it overloads working memory. 

Cognitive load research shows that best practice is to remove redundant information from learning material. Sweller argues: ‘Most people assume that providing learners with additional information is at worst, harmless and might be beneficial. Redundancy is anything but harmless. Providing unnecessary information can be a major reason for instructional failure.’ 

The ‘split attention effect’ The ‘split attention effect’ occurs when learners are required to process two or more sources of information simultaneously in order to understand the material. This might occur, for example, when a diagram is used to explain a concept, but it cannot be understood without referring to a separate piece of explanatory text. In this instance the learner is required to hold both sources of information in their working memory at the same time and to mentally integrate the two. This places a high cognitive load on the working memory, interfering with the ability of the learner to transfer the relevant information to their long-term memory. 

The split-attention effect can be minimised or eliminated by physically integrating separate sources of information so that they do not have to be mentally integrated by the learner.  Sweller, van Merrienboer & Paas argue: ‘Split attention occurs very commonly in instructional contexts. On the basis of dozens of experiments under a wide variety of conditions, the evidence suggests overwhelmingly that it has negative consequences and should be eliminated wherever possible.’

The ‘modality effect’ The 'modality effect' is associated with the split attention effect, but offers an alternative technique to reduce cognitive load than physically integrating separate sources of information. Instead, it is also possible to decrease extraneous load on working memory by using more than one mode of communication – both visual and auditory. Evidence suggests that working memory can be subdivided into auditory and visual streams (Baddeley 1983, 2002; Baddeley & Hitch 1974), so presenting information using both auditory and visual working memory can increase working memory capacity. For example, when using a diagram and text to explain a concept, the written text can be communicated in spoken form. Using both auditory and visual channels increases the capacity of working memory, and facilitates more effective learning.

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Types of Cognitive Load

When Sweller proposed cognitive load theory, he identified two types of cognitive load, with a third added in 1998. 

Extraneous Cognitive Load relates to how the subject matter is taught. High extraneous cognitive load ‘may be fatal to learning because working memory may be substantially exceeded’ (Sweller, van Merrienboer and Paas, 1998). 

Intrinsic Cognitive Load relates to the inherent level of complexity of the learning material. Two factors influence this: the learner’s prior knowledge and the complexity of the material itself. 

Germane Cognitive Load relates to how to promote the transfer of information into the long-term memory through schema construction. 

(from Shift elearning)

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Cognitive Load Theory: ‘the single most important thing for teachers to know’

Cognitive Load Theory has been described by British educationalist Dylan Wiliam as ‘the single most important thing for teachers to know’ (Wiliam, 2017).

Learning is limited by the capacity of working memory, and there are a number of strategies we can use to manage the cognitive load placed on it so that learning is made more effective, more efficient or both. Cognitive load theory sets out principles to do this.

Terry Heick explains the theory in this way: ‘Because short-term memory is limited, learning experiences should be designed to reduce working memory ‘load’ in order to promote schema acquisition.’ (Heick, 2017)

Cognitive load theory is built upon two commonly accepted ideas: 1. There is a limit to how much new information the human brain can process at one time. 2. There are no known limits to how much stored information can be processed at one time. 

The aim of cognitive load research is to develop instructional techniques and recommendations that fit within the characteristics of working memory in order to maximise learning (see Cognitive Load Theory report, Centre for Education, Statistics and Evaluation, Education New South Wales, 2017).

The theory was developed in 1988 by John Sweller. He believes that there are two critical learning mechanisms:  1. Schema acquisition  2. The transfer of learned procedures from controlled to automatic processing.  In his theory, Sweller argues that intellectual mastery of any subject matter is overwhelmingly dependent on these two processes. 

Cognitive load theory assumes that knowledge is stored in long term memory in the form of ‘schemas'. A schema organises elements of information according to how they will be used. According to schema theory, skilled performance is developed through building ever greater numbers of increasingly complex schemas by combining elements of lower level schemas into higher level schemas. There is no limit to how complex schemas can become. An important process in schema construction is automation, whereby information can be processed automatically with minimal conscious effort. Automaticity occurs after extensive practice.

Cognitive load theory provides a general framework for optimal learning that has implications for the way we teach. By controlling the conditions of learning within the materials used for instruction, we can decrease the risk of cognitive overload and refocus the learner’s attention towards schema acquisition, and ultimately transfer procedures from short-term to long-term memory.

The theory supports explicit models of instruction: the teacher shows the student what to do and how to do it, rather than having students discover or construct information for themselves. As John Hattie puts it:

‘The teacher decides the learning intentions and success criteria, makes them transparent to the students, demonstrates them by modelling, evaluates if they understand what they have been told by checking for understanding, and retelling them what they have been told by tying it all together with closure.’ (Visible Learning, John Hattie, 2008)