CHUNKING MODELS OF EXPERTISE: IMPLICATIONS FOR EDUCATION Fernand Gobet
Chunking theories explain how knowledge is preceived and created. Chunks, perceptual/semantic units, are encoded for later retrieval in LTM (Long Term Memory).
In this article explores how 2 different models of chunking theory (EPAM & CHREST) may suggest how to conduct education more efficiently.
The implications for education can be summarized as follows:
Dedication is essential to acquire knowledge to create and grow the perceptual and memory discrimination network. Thus deliberate practice is key, but as the author puts it Practice needs to be tailored to the goal of improving performance, as many invalid or irrelevant chunks can be acquired, which may be counterproductive.
This raises the question about the value of several online tactical trainers that expose the user to random exercises. Are they
Relationship between abstract knowledge and perception is under discussion but it seems failry clear that: perceptual skills, anchored in concrete examples, play a central role in the development of expertise, and that conceptual knowledge is later built on such perceptual skills. So it is important to develop perceptual chunks but without over emphatizing on it.
Their role should be directed to ...acquisition of perceptual chunks, an important role for teachers (both human and artificial) is to direct learners’ attention to the key features of the material to learn because Presenting components of the right size and difficulty will help students direct attention to the important features of the material, and in turn help the acquisition of perceptual chunks that are appropriate....
Chunking theories and expertise investigation as a whole, have demonstrated that transfer is low and just restricted to situations ...when there is an overlap between the components of the skills required in each domain.
Not all chunking theory simulators reached the same conclussions, but ...simulations with CHREST showing that changes within the ordering of the learning set have a rather strong impact on the structure of the discrimination network and on the speed of information retrieval. Thus the order in which curriculum is thaught seems to be relevant.
Productions are rule based, condition-action pairings. It seems fairly demonstrated that chunks are often encoded as conditions to actions. Two conclussions are reached in the study:
Schemata, or templates, are generalizations of chunks. Chunks with slots.
...without variation, schemata cannot be created. For example, in the case of elementary mathematics, presenting a narrow range of problems will hamper the acquisition of a sufficient variety of chunks and links connecting them, and, consequently, schemata are not likely to be formed.
Relation between different types of knowledge is tricky. And it may seem more conceptual than real, as chunking models have not shown clear distinction between Declarative knowledge (knowing what) and Procedural knowledge (knowing how) creation: ...the learning of both types of knowledge occurs incrementally and implicitly Conceptual: ...to give sufficient basis to conceptual knowledge it is necessary to acquire a richly-connected network of links joining productions and schemata, which are accessible through perceptual chunks.
In fact, learning multiple representations requires duplicating the same information in different formats. Although redundancy is certainly an important aspect of human memory and understanding, CHREST draws our attention to the fact that it also has a cost, in particular with respect to the time spent in learning
The use of multiple representations is only one of many learning devices that have flourished with the advent of modern educational technology. ...Chunk-based models actually warn us against any excess of optimism in the use of new technologies, as long as they do not help circumvent the key limiting constants of human cognition (i.e. attention, STM, and learning rates).
There are vast individual differences in people’s cognitive abilities (Ackerman, 1987; Sternberg, 2000), both at the novice and expert levels.
First, while individual differences tend to be diluted by large amounts of practice, they play a large role in the early stages of studying a domain, which characterizes much of classroom instruction. Second, as seen earlier, taking into account individual differences may lead to better instruction, because instruction can be optimized for each student, including feedback on progress, organization of material, and choice of learning strategies to be taught.