Editing Chunking

== '''Overview''' ==
Chunking involves breaking information into smaller pieces, identifying similarities or patterns, and re-organizing and grouping information into manageable units for effective [[transfer]] from working memory (WM) to long-term memory (LTM) <ref> Martinez, M. (2010). The Cognitive Architecture. In Learning and Cognition: Design of the
Mind</ref>. This information processing model is vital to learning design as it:
# overcomes the limitations of WM and prevents cognitive overload while processing large amounts of information
# organizes complex information into recognizable patterns that can be learned and recalled easily
Thus, chunking enables developing expertise by integrating small units of information into chunks that can be expanded and redefined over time.

== '''Example''' ==
To illustrate this, in Fig 1, learning a 10-digit number can be difficult - but chunking it into smaller groups (4 chunks)
of recognizable patterns by adding context makes it easier to remember and recall. It also reduces the working memory's load, which has to learn 4 instead of 10 units of information. Consequently, this number is encoded as one chunk of 4 smaller chunks of information that can be associated with the big chunk of contact numbers that consists of declarative knowledge (facts), and the
association eventually becomes automatic.

== '''Evidence''' ==
For efficient application of chunking, Gobet <ref> Gobet, F. (2005). Chunking models of expertise: implications for education. Applied Cognitive Psychology, 19(2), 183–204. https://doi.org/10.1002/acp.1110 </ref> stated that domain-specific perceptual chunks would be vital for developing expertise and building conceptual skills eventually. Chase & Simon <ref> Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81. http://dx.doi.org/10.1016/0010-0285(73)90004-2 </ref> recognised the value of chunking while studying expert chess players who identified the recurrent patterns in pieces on the chessboard than novices resulting in playing better moves without an extensive search. Chunking is also prevalent as a learning strategy in arts, sports, sciences, and professions <ref> Richman, H. B., Gobet, F., Staszewski, J. J., & Simon, H. A. (1996). Perceptual and memory processes in the acquisition of expert performance: the EPAM model. In K. A. Ericsson (Ed.), The road to excellence. Mahwah, NJ: Erlbaum. Simon, H. (1974). How big is a chunk? Science, 183, 466–482. </ref>

== '''Critique''' ==

However, it still remains unclear how many chunks can be stored in the working memory at one time<ref> Gobet, F. (2005). Chunking models of expertise: implications for education. Applied Cognitive Psychology, 19(2), 183–204. https://doi.org/10.1002/acp.1110 </ref> - with claims ranging between 4 <ref> Cowan, N (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, pp. 87-185 </ref> to 9 </ref> Miller, G. (1956, March). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2), 81–93. </ref> units of information. Secondly, while using non-unique elements while chunking, Thalmann et al <ref> Thalmann, M., Souza, A. S., & Oberauer, K. (2019b, January). How does chunking help working memory? Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(1), 37–55. https://doi.org/10.1037/xlm0000578 </ref>  concluded that there was a smaller chunking benefit when individual elements of the chunk were repetitive. Furthermore, in the same experiment, chunked material learned earlier had a better recall than the chunks at the end. Lastly, Gobet <ref> Gobet, F. (2005). Chunking models of expertise: implications for education. Applied Cognitive Psychology, 19(2), 183–204. https://doi.org/10.1002/acp.1110 </ref> remarked that learning irrelevant chunks could hinder performance, thus postulating the need for deliberate practice.

== '''Conclusion''' ==
While we know that chunks improve learning to regroup, strengthen, and connect knowledge, it is critical to understand contexts in which chunking may not be helpful to learning design. Furthermore, it is worth comparing learning complex chunks in small
numbers and simple chunks in larger numbers to understand the definitive nature of chunks and learning chunking more effectively

== '''References''' ==