The Acquisition of Intellectual Expertise: A Computational Model

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Intellectual expertise is ability and knowledge that a person has that allows them to solve tremendously complex problems. It is important to appreciate how people become experts so that we can enhance educational strategies, and help learners achieve their full academic potential. Unfortunately, the process of acquiring intellectual expertise is not well understood. The objective of this research is to build and test a computational theory of this process. My approach is to train artificial neural networks (ANNs) as a model of expert human learning. ANNs address many of the difficulties found in trying to study expertise in humans; they have previously been successful in modeling other types of human learning. In completed work, I have built a first version of the model and used it to confirm two hypotheses:

(1) An artificial neural network will be used as a model to examine how people learn under different training scenarios.

(2) Different methods for delivering the training material result in different final performance, and best performance is achieved by incrementally increasing the complexity of the material.

I propose to complete the dissertation by testing a third hypothesis: Different delivery methods result in different conceptual development and internal conceptual representations, which in turn is responsible for the different performance. The results suggest that psychological learning theory could be considered when designing instructional strategies. My research has shown that, in contrast with symbolic models of cognition, connectionist models are able to model the human learning procedure, and give practical insights into how people should be trained.