Borrowing, Reorganising, and Randomness: Cognitive Load’s Principles of Learning

In cognitive load theory, there are 5 underpinning biological evolutionary principles which inform the basis of an established cognitive architecture. These principles are the information store principle, narrow limits of change principle, borrowing and reorganising principle, randomness as genesis principle and the environmental organising and linking principle. In this article, I will be walking you through the borrowing and reorganising principle as well as the randomness as genesis principle. You can find a link to my previous article about the information store and narrow limits of change principle here.

In the noble pursuit of knowledge, understanding how humans learn and develop is underpinned by two foundational principles: the Borrowing and Reorganising Principle and the Randomness-as-Genesis Principle. These principles explain how individuals build on what is already known and how new knowledge is generated when existing understanding is exhausted. Both concepts have highly profound implications for teaching, learning, and the development of efficient instructional methods.

The Borrowing and Reorganising Principle

What is “Borrowing”?

Humans do not build their own knowledge. Seemingly controversially, we acquire most of our knowledge from others. Research rightly suggests that almost all of the knowledge held in an individual’s long-term memory has been borrowed from the long-term memory of other individuals. In this context, “borrow” means the transfer of knowledge – humans transfer knowledge from one person to another. This transfer occurs as we listen, observe, read, and imitate others, borrowing their knowledge and organising it into our own memory systems.

Borrowing allows us to bypass the highly inadequate inefficiencies of trial-and-error learning by using the accumulated wisdom of others. This shared, felicitous cumulative knowledge base forms a body of cultural and cognitive knowledge passed from generation to generation, ensuring the preservation of our most helpful information. While this information is borrowed, it is rarely borrowed without being reorganised.

What is “Reorganising”?

Borrowed information is rarely stored and retrieved passively. Instead, it is reorganised with existing knowledge already stored in long-term memory. Reorganised in this context means that incoming knowledge is evaluated to “fit” in with existing ideas. This process is best understood through schema theory. A schema is a mental template or story that helps people understand and process the world by organizing related pieces of information into coherent wholes such that the brain will connect the dots presented to it into an existing schema to make sense of what is occurring. For example, Try and guess where I am:

• I walk into a large room with people seated in orderly rows.
• They’re dressed in a mix of casual and business attire.
• There’s a soft hum of conversation — someone asks, “Do you think we’ll need to take notes?”
• A large clock hangs on the wall above a whiteboard.
• Some people have notebooks open, pens poised. Others glance nervously at the front.
• A figure enters. The room goes still.
• She says, “All rise.”
• Everyone stands. She nods: “You may be seated.”
• Then, from the front of the room: “How do you plead?”

This example demonstrates schema theory. Initially, the schema that was activated was possibly a university classroom or adult training, but once all rise was said a different schema was activated and tested, until the schema was confirmed by the question “how do you plead?” This, while a simple example, shows that information must be attached to ideas that are known. 

When reorganisation occurs, it involves both highlighting information that fits with prior knowledge while discarding or de-emphasising information which does not. In this way, all meaningful learning is integrated into existing memory – facts cannot be learned in isolation; they must be connected to a schema to be understood and recalled later (a characteristic of the Matthew effect).

Why does borrowing and reorganising matter?

Facts can’t be learned in isolation — that’s not a theory, that’s a myth. True learning occurs through borrowing and reorganising information into existing mental frameworks. If a student remembers an energy system but not the characteristics of it, this doesn’t demonstrate that they’ve learned disconnected facts. It simply shows that only the energy system name was remembered — not that the knowledge was isolated, but that the schema was never fully formed. The idea that facts can exist meaningfully on their own falls apart, because even recalling an energy system implies some connection to some “thing”. 

Moreover, borrowing and reorganising is the most efficient way to maintain and advance our collective knowledge base. If each generation had to rediscover knowledge from scratch, then progress would be impossible. Therefore, we should aim to transfer as much of our accumulated knowledge as possible to younger generations, quickly and effectively, so they can push beyond the edge of what is already known.

How does borrowing and reorganising affect cognitive load?

From a cognitive load perspective, borrowing and reorganising significantly reduces working memory demands, freeing up more cognitive resources to process information from the working memory to the long-term memory. By providing learners with structured and organised, they have a significantly higher chance of integrating it with existing schemas. This approach minimises wasting cognitive resources and allows for more efficient schema construction in long-term memory.

The Randomness as Genesis Principle

What is randomness as genesis?

While most knowledge is borrowed, some knowledge must be created. The Randomness-as-Genesis Principle explains how new knowledge is created when no existing information is available to solve a problem. In such cases, individuals must engage in a random generate and test procedure. This means they try random or partially informed solutions, rank and test them for effectiveness, accept they are effective and incorporate them into their memory (if successful) or acknowledge they are not effective and discard them and begin again.

This process is cognitively demanding and woefully inefficient, but it is sometimes neccesary—particularly when dealing with completely novel problems which are found at the edge of the collective of human understanding (unlikely to be in school). 

How does randomness as genesis affect cognitive load?

Random generation and testing imposes a high extrinsic cognitive load on learners. Until a workable solution is found, no meaningful learning such that schema construction and integration occurs, has been completed. Accordingly, the reorganisation principle actively rejects this information because it cannot yet be integrated into long-term memory.

“This can be achieved through explicit instruction and multiple cognitive load effects, such as worked examples like the example-problem pair approach… This approach is most effective, according to the current literature, when fully worked examples are studied, removed from the student and then the same or very similar question follows.”

What does this all mean for my teaching?

The teaching strategies and curriculum must be designed to minimise the need for randomness-as-genesis and maximise borrowing and reorganising. This can be achieved through explicit instruction and multiple cognitive load effects, such as worked examples like the example-problem pair approach. This approach is most effective, according to the current literature, when fully worked examples are studied, removed from the student and then the same or very similar question follows. Thus, it is called the example-problem approach. This approach heavily relies on the borrowing and reorganising as students borrow the knowledge of the expert teacher through the worked example and attempt to integrate it into their own schema.

Other attempts to make this process more efficient have been examined as well such as guidance fading effects. Guidance fading effect essentially means slowly increasing the responsibility of the learner to complete the task, similar to a practical application of the well-known gradual release of responsibility concept. While earlier cognitive load studies have seen the benefit of guidance fading effects during written work, others using a power point to demonstrate these guidance fading examples have not. Unfortunately, the science remains unclear of the benefits of guidance fading. Of note, faded examples have not been tested for paragraph writing, it has only been tested in mathematical domains, so there is a tenuous cognitive leap that you are being invited to take in relation to HMS and paragraph writing. If this leap is too uncertain, the traditional worked example- problem pair process should be followed (this is supported by the literature) and at the very least have one fully worked example available to study then removed (or not) so students can complete the same question independently. 

On the other hand, when students are left to discover or construct knowledge without guidance, they are forced into high-load, inefficient generate-and-test procedures. While some people may believe this has a latent learning effect, it more often results in confusion, frustration, cognitive overload and significantly less integration into existing schema, and consequently limited meaningful learning.

It is known effective instruction should lean heavily on the borrowing and reorganising principle and not the randomness as genesis principle. This ensures students build on what is known, reduce cognitive load, and accelerate the accumulation of knowledge. Random generation has its place, particularly in research and innovation – not in schools. Using random generation as a teaching strategy isn’t building knowledge – it’s building failure. 

References – Further reading

Chen, O., Retnowati, E., Chan, B. K. Y., & Kalyuga, S. (2023). The effect of worked examples on learning solution steps and knowledge transfer. Educational Psychology, 43(8), 914-928. https://doi.org/10.1080/01443410.2023.2273762 

Kyun, S., Kalyuga, S., & Sweller, J. (2013). The Effect of Worked Examples When Learning to Write Essays in English Literature. The Journal of Experimental Education, 81(3), 385-408. https://doi.org/10.1080/00220973.2012.727884 

Renkl, A., & Atkinson, R. K. (2010). Learning from worked-out examples and problem solving. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 91–108). Cambridge University Press. https://doi.org/10.1017/CBO9780511844744.007

Smith, H., Closser, A. H., Ottmar, E., & Chan, J. Y. C. (2022). The impact of algebra worked example presentations on student learning. Applied Cognitive Psychology, 36(2), 363-377. https://doi.org/10.1002/acp.3925 

Sweller, J. (2006). The worked example effect and human cognition. Learning and Instruction, 16(2), 165-169. https://doi.org/10.1016/j.learninstruc.2006.02.005 van Gog, T., Kester, L., & Paas, F. (2011). Effects of worked examples, example-problem, and problem-example pairs on novices’ learning. Contemporary Educational Psychology, 36(3), 212-218. https://doi.org/10.1016/j.cedpsych.2010.10.004

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