Knowledge-Based Flow of Control in Computer-Aided Learning
Geoffrey I. Webb · 1986
In this paper I examine the utilisation of knowledge representation in Computer-Aided Learning (CAL) with the aim of establishing knowledge-based CAL techniques that are best suited to current technology. Most existing knowledge-based CAL systems attempt to generate the entire instructional sequence directly from a domain knowledge base. Such systems suffer from several limitations. These limitations include: 1. It is questionable whether the techniques exist to produce such systems for any but a highly restricted set of domains. 2. Even for those domains in which such systems can be produced the overheads are prohibitive for most purposes. Given these limitations, I argue that knowledge representation should be utilised in CAL only for those aspects of the instructional process for which it results in substantial gains without prohibitive overheads. I demonstrate that one aspect of CAL for which this holds is for managing flow of control within instructional material. I provide a detailed description of feature networks. These are a variant of M.A.K. Halliday’s system network formalism. Feature networks are a knowledge representation formalism that efficiently encodes exactly the knowledge that is required for knowledge-based flow of control. It is shown that computer based lessons that utilise feature networks for control flow of control are extremely economic in terms of both authoring time and computer resources while providing highly responsive tuition. DABIS, a system that embodies the methodology outlined above, has been implemented and is described.