Unlocking the power of chunking: Reducing cognitive load

As teachers, we often see students struggle to process and retain large volumes of information. One proven technique to help students manage complex content and improve memory retention is chunking. But what exactly is chunking, and how can we apply it in today’s classrooms? 

What is chunking? 

At its core, chunking refers to the process of breaking down large amounts of information into smaller, manageable "chunks" which act as a scaffold for our novice learners. This technique takes advantage of how our brains naturally organise and process information, helping students grasp complex concepts more easily and maximise learning efficiency. 

For example, remembering a 10-digit phone number is easier if it's split into smaller groups (e.g., 0412 345 xxx), rather than as one long sequence. Similarly, chunking in the classroom involves grouping related pieces of information into a logical sequence, making them easier for students to understand, recall, and connect into their mental model or schema. 

Why Chunking Works 

Cognitive psychology tells us that working memory can hold about seven chunks of new information, and can only work on four (Miller, 1956). When we overload students with information, it can lead to cognitive fatigue and disengagement. This is where chunking comes in: by breaking content into smaller units, we reduce the mental load, enabling students to move information from working memory to long-term memory more efficiently.

“Chunking helps students use their working memory effectively, storing information for long-term retrieval. In a multi-tiered system of support, our chunked approach supports the needs of the majority of students, with additional strategies like diagnostic assessments to target the needs of the remaining 10–20%.”

The link to Cognitive Load Theory

Cognitive load theory, developed by John Sweller (1988), explains how learning is most effective when cognitive load is kept within manageable limits. Chunking directly addresses this by breaking down complex concepts into bite-sized portions that students can process without being overloaded which also leads to feelings of overwhelm. 

Benefits for student learning 

Recent reports, like the AERO Evidence-based Teaching Practices (2023), highlight the importance of structuring learning in ways that reduce cognitive overload. The report outlines how techniques like chunking help students make connections between new and existing knowledge, ultimately improving comprehension and retention. Research within this framework aligns with the principles of cognitive load theory, stressing that teachers must actively manage the way they present information to avoid overloading students’ working memory. 

Moreover, a study by the Australian Council for Educational Research (ACER) (2022) found that chunking not only supports better recall but also promotes greater active engagement. When students can make connections within the material, they are more likely to engage with it meaningfully, leading to deeper learning. 

Implementing chunking in the classroom 

There are a variety of ways to use chunking to enhance learning outcomes in your classroom: 

1. Break content into sections Divide lessons into smaller, focused and clearly articulated chunks. For instance, if teaching a lesson on adding and subtracting fractions, the chunks might look like: 
   • I can add and subtract fractions with the same denominator 
   • I can add and subtract fractions with related denominators 
   • I can add and subtract fractions with any numerical denominator 
2. Use visual aids encourage students to create visual aids—like mind maps or graphic organizers—to help them group and relate information. Visual cues support the chunking process, making abstract concepts more concrete and easier to understand. 
3. Prompt self-chunking teach students to chunk content themselves. Encourage them to identify key ideas, highlight important facts, and group them into categories. This encourages active learning and helps them internalize the material. 
4. Pace the learning space out the delivery of new content into clearly defined discrete tasks, giving students time to process one chunk before moving on to the next. This pacing ensures that students have time to consolidate learning, leading to better long-term retention. 
5. Retrieval practice after introducing a chunk, ask students to recall key concepts before moving on. This retrieval practice solidifies the connections in their memory and helps with retention. It's a technique backed by extensive research, including findings in the AERO report (2023). 

Conclusion 

Chunking is a simple but powerful tool for reducing cognitive overload and enhancing student learning. By breaking down complex information into smaller, meaningful units, you help students manage cognitive load and improve memory retention. This practice not only supports better outcomes in the classroom but also empowers students to become more active, independent learners. 

Lindy Sharkey 

Mathematics Portfolio Manager at Pearson

Lindy is our lead Mathematics Publisher, with over fifteen years’ experience in education, starting as a mathematics teacher. She has also previously been an author on Pearson Mathematics, Pearson Diagnostic (our digital assessment tool) and Pearson Queensland Senior Mathematics. She is a regular contributor to the development of assessment and curriculum for the VCAA, and is a columnist for The Mathematical Association of Victoria’s Vinculum.

Suggested sources for further reading: 

• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. 

• Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. 

• Australian Education Research Organisation (AERO) (2023). Evidence-Based Teaching Practices: A Framework for Educators. 

• Australian Council for Educational Research (ACER) (2022). Improving Learning: The Power of Cognitive Load Theory.