Rule generation from neural networks for student assessment

Abstract

HyValue is a hybrid electronic submission system which utilizes techniques from natural language processing, neural networks and rule based systems to accept, evaluate and mark work submitted by a student for reading or writing. This paper describes the theory behind the system design and the development of the individual components and their interaction. Issues addressed include the definition of sentence structure, fuzzy rule construction and integration with a knowledge base containing the marking rubrics for reading and writing. An evaluation of the system is provided and conclusions drawn.