Understanding the Information Store Principle and the Narrow Limits of Change Principle Cognitive Load Theory (CLT) is built upon several foundational principles that explain how human cognition processes and retains information. Two foundational principles within CLT are the Information Store Principle and the Narrow Limits of Change Principle. These principles clarify how working memory and long-term memory function in learning and problem-solving.
The Information Store Principle describes that human cognition is fundamentally reliant on long-term memory as an extensive store of knowledge. This principle assumes that experts in a domain do not solve problems by engaging in complex calculations or reasoning in real- time. Instead, they retrieve previously learned ideas from long-term memory to respond to familiar situations. For instance, a chess grandmaster does not need to analyse each possible move “in real time”. Rather, they recall familiar board configurations and select the optimal response from thousands of stored schemas in long-term memory. To clarify, chess grand masters are not highly critical thinkers, they are simply retrieving from learnt experiences (Sweller, et al., 2011, p19-22).
The Narrow Limits of Change Principle, on the other hand, highlights the constraints of working memory. Unlike long-term memory, which has an infinite capacity, working memory is extremely limited. It is established knowledge that most people can hold and manipulate only about 3-5 elements of novel information at a time. This limitation means that learning new and complex information must be managed carefully, ensuring that cognitive load does not overwhelm working memory’s capacity.
What’s the Cost of High Cognitive Load on Working Memory?
Given that working memory has strict limits, it is highly susceptible to cognitive overload. When the cognitive load of a task is too high whether due to excessive complexity, irrelevant information, or a lack of prior knowledge—learning becomes inefficient or even impossible.
For example, students presented with an advanced algebra problems without prior foundational knowledge may struggle to process the information, as their working memory is overloaded with novel elements. Each operation and letter (i.e +, -, , y) and how they relate to each other is a novel element.
Moreover, while students tend to benefit from similar instructional strategies, it is also important to recognise individuals differ in their working memory capacities. Some learners naturally have a higher working memory capacity, allowing them to handle greater amounts of novel information at once. However, those with lower working memory capacity are more vulnerable to cognitive overload, making it difficult for them to process complex material without appropriate instructional support. Understanding these differences and how to address them without lowering the learning standard is crucial in designing effective learning environments.
How does long term memory and working memory support each other?
A driving idea of CLT is that the most effective way to manage cognitive load is to develop and use schemas in long-term memory. Schemas allow working memory to efficiently process complex information by reducing multiple elements into a single element. This process is particularly beneficial for learners with lower working memory capacity, as it reduces the number of elements their brain needs to engage with at any given time.
For example, a novice learning a new mathematical concept might struggle with each step individually, leading to high cognitive load. However, an expert who has already developed a schema for that concept perceives the entire problem as a single element, significantly reducing the working memory demand. This explains why structured learning approaches that emphasise schema development, such as worked examples, are more effective than unguided or minimally guided learning, particularly for novice learners.
So….CLT operates under a file storage analogy and bottleneck theory?
A common misconception about CLT is that it promotes an outdated computer file storage analogy or a bottleneck theory of cognition. In reality, CLT does not suggest that human cognition functions like a basic information-processing machine where knowledge is retrieved in a linear manner from a hard drive. Instead, CLT emphasises that the development of long-term memory directly supports working memory, enabling it to process greater amounts of information more efficiently.
This view is supported by extensive research across multiple disciplines, including psychology, neuroscience, and education. Studies in expertise development demonstrate that the more information stored in long-term memory, the less burdened working memory becomes. This insight contradicts the notion that working memory is permanently constrained by a rigid bottleneck. Rather, working memory has no known capacity or duration limits when dealing with information transferred back from long-term memory.
How does all this theory actually help my teaching and students?
With the introduction of the new HMS (Health and Movement Science) syllabus, educators must carefully consider the instructional methods that align with the cognitive architecture of novice learners. Two dominant instructional approaches—inquiry learning and explicit instruction—have vastly different implications when viewed through the lens of CLT. Acknowledging that there may be differently defined systematic procedures of Inquiry learning, this instructional approach tends to emphasise student-driven exploration, problem-solving, and discovery before explicit instruction. While this approach can be beneficial for students with high prior knowledge, or high working memory capacity, who can effectively integrate new information into existing schemas, it is often problematic for novices. Without sufficient guidance before a problem is posed, inquiry learning imposes a heavy cognitive load, making it difficult for students to retain and apply new information effectively because of the focus on the cognitive resources used by the search for information and test process
(aka random-generate-and-test process).
Explicit instruction, in contrast, aligns more closely with CLT principles. By directly teaching essential concepts, providing worked examples, and gradually scaffolding learning tasks until independent learning can occur, explicit instruction helps students build schemas in long-term memory. This structured approach minimises the use of cognitive resources on search processes through unnecessary cognitive load and supports all novice learners, especially those with lower working memory capacity. In the context of the HMS syllabus, explicit instruction ensures that students acquire foundational knowledge before engaging in inquiry- based learning.
Moreover, CLT suggests that inquiry-based learning that includes new skills, such as literature reviews, experiments, surveys (etc.), ethical approaches, description of results, referencing, bibliography annotation, note taking for conversations and annotating conversations, should all be modelled and explained before students engage in this activity independently. By aligning instructional strategies with cognitive architecture, we can optimize learning outcomes and provide students with the necessary tools to navigate complex domains with confidence and expertise.
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