Chunking as a pedagogy

Chunking involves breaking up new learning into discrete sections in order to avoid cognitive overload, and to promote schema development.  

Using cognitive science to explain learning  

In cognitive architecture, the working memory (WM) is responsible for processing or encoding new information. A part of this processing involves searching the long-term memory (LTM), which stores knowledge in the form of networks of interrelated ideas, called schemas to ascertain if there exists any related information.

The processing places a cognitive load on the WM (Sweller et al 2019), which is generated in two ways: through the inherent complexity of the task, called the intrinsic cognitive load, and through the environmental influences that distract attention, called the extraneous cognitive load. The amount of cognitive load the typical WM can handle is determined by the limited capacity of the WM (Cowan, 2001), understood to be approximately four complexities (or four individual components that require processing) at a time, but also by how much of the inherent load can be connected to what knowledge already exists in the long-term memory (LTM).  

The connection of new knowledge with existing knowledge reduces cognitive load because the existing knowledge effectively eliminates, or reduces, the effect of one or more of the complexities consuming the WM load (Ericsson & Kintsch, 1995). To put it simply, the more the connection, the less the cognitive load.  

Hence, two students presented with the same information may have different experiences in learning it, as one may have sufficient related knowledge in the LTM and so mitigate the inherent cognitive load in the WM, whereas another student may not be able to relate any of the new learning to existing knowledge and so usurp all the available WM capacity, to the point of becoming cognitively overloaded. This overloading is when little or no learning is possible.  

The video below, created by Aaron Honson and Georgia Forrest, highlights the increased efficiency of learning when a schema can be called upon in a new learning context. A detailed explanation of what is happening in the video can be read here.  

Designing learning for efficiency 

Preventing overloading is why reducing extraneous distraction is so important in any learning context, but also why novices to a topic require significantly more scaffolding when learning something new: their schema is weak, so their WM is exposed by its limitation of capacity. The scaffold provides the WM with the time and space to process information without becoming cognitively overloaded.  

Chunking breaks up the strain on the WM by providing a scaffold. It assists students in moderating the cognitive load experienced during the act of trying to maintain attention when presented with a new learning context. It is important to note however that attention span is not dictated by time, but more by the need to maintain the WM’s capacity to process cognitive load. The greater the ability of the learner to draw from existing schema then the greater the amount of time attention can be maintained, and the longer the chunk of information presented can be made. For example, when doing work that is familiar to us, we may be able to concentrate for hours at a time, but when learning new and complex ideas our ability to maintain active attention will be significantly reduced.  

But also of note is the need for learning to be active, as this engages the LTM and requires the retrieval of knowledge, which facilitates the strengthening of the neural pathways to memory. ‘Active’ learning also helps students to think about what has just been presented and process it more deeply before moving on to the next idea. Willingham (2003) exhorts this practice, succinctly summarised in his statement: you remember what you think about.  

Developing schema

When sequences of learning are chunked in this way to enhance deeper thinking, natural boundaries are created around key concepts. The boundaries serve as nodes in the schema for the topic, facilitating the increased opportunity for retrieval of knowledge as each node in a schema can trigger another. In other words, one memory related to a concept wanting to be remembered can be cued by remembering another in the schema. The more easily existing knowledge can be retrieved, the less a student will become cognitively overloaded.  

The combination of the power of active learning and the establishment of boundary nodes in a schema have clear implications for the design of sequences of learning. Content that is delivered didactically for extended periods of time that cumulatively contains too much intrinsic load for the working memory to handle is less effective than the same content being broken up and acted on so that the intrinsic load of each new idea does not exceed the working memory capacity.  

What good teachers have always done 

Good teaching has always intuitively broken new learning up, incrementally building and developing skill before another complexity in the material is presented. The mode in which chunking occurs is irrelevant: chunking in a lecture by stopping after a key concept is taught and having students act on it, or chunking in an online setting by providing re-playable short videos that are followed by activities that promote the making of connections between what is presented and previous knowledge. Now, what we knew intuitively about the practice of good teachers is supported by evidence-based cognitive science.  

Chunking the delivery of content isn’t about catering for ‘weak generational attention span’, it’s to maximise learning efficiency.


Cowan, N. (2005). Working memory capacity. New York: Psychology Press. 

Ericsson, K., & Kintsch, W. (1995). Long-Term Working Memory. Psychological Review, 102 (2), 211-245. 

Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261–292 

Willingham, D. (2003). Ask the Cognitive Scientist: Students Remember…What They Think About. Retrieved from on 9/2/23 

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