GST 1 is a module at Maynooth University which aims to improve research skills and employability. To gain 5 ECTS for this module you need to attend 6 sessions and produce a diary entry or set of notes for each one.
Session on Accelerated Expertise and Superintelligence presented by Dr D Delany from Trinity College, Dublin.
The cognitive science of expertise, competence is a function of mental model quality. Our mental model of the world and our field is a schema or knowledge structure. This is particularly interesting as it ties in with my education research into the SOLO taxonomy and gives a possible reason why it may be effective.
The ability to think in the abstract relies upon the cognitive tools we have available. The shift from orality to literacy marked a fundamental cognitive shift. Alphabetic literacy, as opposed to logographic, was more efficient and is linked to the rise of abstract theoretical and scientific thinking.
The rise in literacy and literature is linked to how the individual parses the information. Therefore the capacity for abstract thought is a function of literacy.
Dr Delany suggested what he called ‘knowledge engineering’ – aiming to reverse engineer the understanding beneath a concept. For example, to understand the concept of capital and its associated concepts.
Capital: The man-made factor of production encompassing all the physical assets, such as machinery, used by a business to produce goods and services.
Factor of Production: Resources, such as capital, used by a business as inputs to the production process in the creation of goods and services.
By considering the definitions we can extract the deep learning, e.g. that production processes use factors of production to create goods and services, and to demonstrate a level of expertise. Extracting the deep structure of concepts enables us to compare and critique experts in the field.
Considering what lies beneath the sentence level – the sentence level structure may reflect orality.
Levels of analysis
- obscures deep structure
- weakly schemogenic
- exposes the deep structure
- functional role of concepts
- strongly schemogenic
Creating a Semantic Relationship
Taxonomic – class and subclass (is a…)
Holonymic (x contains y)
Meronymic (y is a part of x)
Going Beyond Expertise
The mental model can be explored by using adduction to infer what else the definition suggests. For example, if there are man-made productive assets it suggests that there would also be synthetic productive assets.
One of the challenges of using this type of reverse engineering is overcoming conceptual biases. Human creativity is relatively limited in that we recycle and adapt familiar elements.
The example we explored in the session was Newton’s first and second law. Newton’s first law was modified from Descartes’ Laws of Nature 37 and 39. What was particularly interesting is that the second law can be extrapolated from the first, but this took Newton years to do.
Learning transfer – the ability to apply knowledge learned in one context in new contexts – is the key to adaptive expertise. This is where I noticed the similarities between the different levels within the SOLO taxonomy. Weakly schemogenic learning can lead to two problems: the ‘incompetent novice’ whose knowledge is not linked, or in small clusters (SOLO Uni-structural and Multi-structural), and the ‘brittle expert’ whose knowledge is linked by not consistently, or is hierarchical and narrow (SOLO Multi-structural and Relational). The adaptive expert’s knowledge is integrated, hierarchical and extensive (SOLO Extended Abstract). In strongly schemogenic learning the working memory is directly linked to schemas.