## Key Ideas > [!abstract] Core Concepts > > - **Related information organised into meaningful units**: Multiple separate pieces of knowledge combine into single, functional chunks stored as schema (Miller, 1956; Chase & Simon, 1973) > - **Increases effective working memory capacity**: 12 random letters overwhelm memory, but "HIPPOPOTAMUS" becomes one chunk (Miller, 1956) > - **Automaticity develops through practice**: Familiar information requires less working memory than new information (Ericsson & Kintsch, 1995) ## Definition **Chunking**: Organisation of related information pieces into meaningful units (schema), allowing multiple elements to be processed as single items in working memory, overcoming capacity limitations (Miller, 1956; Cowan, 2001). ## Connected To [[Schema]] | [[Part-whole approach]] | [[Cognitive Load Theory]] | [[Memory]] | [[Fluency]] | [[Practice]] | [[Curse of Knowledge]] --- ## How chunking works Chunking affects how the brain processes information and working memory capacity (Cowan, 2001). New information occupies more working memory capacity than familiar information. Knowledge organised in long-term memory as schemas can address working memory limitations (Sweller, van Merriënboer, & Paas, 2019). Multiple related pieces of information become treated as a single unit, reducing cognitive load (Miller, 1956). The ease of recalling chunked knowledge ([[Fluency|automaticity]]) increases with [[Practice|practice]], eventually requiring minimal conscious effort (Ericsson & Kintsch, 1995). ## Expert versus novice chunking Experts and novices process information differently through chunking (Chase & Simon, 1973; Chi, Feltovich, & Glaser, 1981). Expert mathematicians view adding fractions as a single, discrete skill because they have chunked together identifying the least common multiple, producing equivalent fractions, multiplicative reasoning prerequisites, and simplification procedures. Novice students experience each component as a separate working memory load. Each element competes for limited cognitive resources (Cowan, 2001). The task quickly overwhelms capacity. The same task that takes experts seconds can leave novices struggling for minutes because they process different numbers of elements (Chi et al., 1981). ## Implementation Teachers should organise related concepts into meaningful groups through explicit connections and extensive practice. After individual components are taught separately, combine them systematically using a [[Part-whole approach]]. Analogies, concrete examples before abstract concepts, and highlighting relationships between elements help students chunk information. This enables complex thinking by treating multiple elements as single units. ## Key warnings and pitfalls Teachers cannot force chunking. It develops through understanding relationships and practice (Ericsson & Kintsch, 1995). Expert teachers affected by the [[Curse of Knowledge]] often underestimate chunking requirements for novices (Hinds, 1999). Incomplete chunking creates unstable knowledge that collapses under pressure (Cowan, 2001). Students may appear to chunk but only memorised surface patterns without underlying understanding (Chi et al., 1981). ## Practical examples The letters C-A-T become a single concept through chunking rather than three separate symbols (Miller, 1956). The mathematical expression "3x + 5 = 14" becomes a meaningful unit rather than five separate symbols requiring individual processing. Solving linear equations chunks the equality concept, inverse operations, maintaining balance, and algebraic notation into a single problem-solving procedure. ## References Chase, W. G., & Simon, H. A. (1973). Perception in chess. *Cognitive Psychology*, 4(1), 55-81. https://doi.org/10.1016/0010-0285(73)90004-2 Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. *Cognitive Science*, 5(2), 121-152. https://doi.org/10.1207/s15516709cog0502_2 Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. *Behavioral and Brain Sciences*, 24(1), 87-114. https://doi.org/10.1017/S0140525X01003922 Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. *Psychological Review*, 102(2), 211-245. https://doi.org/10.1037/0033-295X.102.2.211 Hinds, P. J. (1999). The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance. *Journal of Experimental Psychology: Applied*, 5(2), 205-221. https://doi.org/10.1037/1076-898X.5.2.205 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. https://doi.org/10.1037/h0043158 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. https://doi.org/10.1007/s10648-019-09465-5 ---