This post is the third of four, where I share with you the presentation I gave at the recent ResearchEd conference in Auckland, New Zealand.
When teaching children to read and write in English, as teachers, our problem lies in devising efficient instructional procedures for teaching the complexities of the English alphabet code, and for transmitting knowledge to the long-term memories of learners.
Sweller, Geary, and others contend that, as humans, we have evolved to assimilate and process information in order to direct human action. In so doing we have evolved what they refer to as a natural processing system: human long-term memory. This repository is not a jumble of unrelated facts we store and retrieve from time to time. It is absolutely central to how we learn. The problem lies in getting information from working memory into long-term memory in the first place.
Getting information from working memory, where it only resides for about twenty seconds into long-term memory is the challenge. The analogy I like best is from professor Helen Abadzi, of the University of Texas, who once described the difficulty of getting information from working memory into long-term memory as being like squeezing something through the very, very long neck of a bottle, whose bulb has infinite capacity.
When we ask ourselves what it is that differentiates experts and novices in solving particular classes of problems, by which I mean domain-specific problems, such as learning to read and write, the answer appears to be simply the amount of knowledge or information held in long-term memory. To be skilful in any domain, it is necessary to have at one’s disposal a huge stock of knowledge that is instantly available.
When we present young children with the task of learning to read and write, they may have little or no prior knowledge of this novel information. In addition, working memory is transient and limited. What we must do therefore is to present them with an approach that builds a schema for sounds and individual spellings and teaches them from simple to complex, building on that schema and turning it into an ever more sophisticated tool.
Schemas organise and store knowledge. They categorise the elements of information we want to teach according to the way in which we want them to be used. They also reduce working memory load because, as Anders Ericsson and his colleagues have long argued, even a highly complex schema can be retrieved from long-term memory and dealt with as if it is a unitary element in working memory.
To develop a conceptual understanding of how the alphabetic code works, the schema must also be made explicit. And, we also need to teach beginning readers the procedural skills of segmenting, blending, and phoneme manipulation, which enable learners to use the factual knowledge they are acquiring.
Because schemas can incorporate facts, functions, procedures and entities specific to a domain, the building of schemas for elaborated domains, such as English orthography, is a long process and there are no shortcuts. All the literature Ericsson and his colleagues have assembled in The Cambridge Handbook of Expertise and Expert Performance tells us the same story: development of individual performance in any domain is ‘relatively slow and graduated even when a large amount of time is invested’.