Anneli Edman

A combined knowledge and hypermedia system to attain educational objectives

A knowledge system can be a valuable educational tool, however there are four principal problems: lack of control and initiative, difficulties in understanding the reasoning process, absence of overview and problems in comprehending the complexity of the domain. By merging techniques from knowledge and hypermedia systems these problems may be alleviated. This paper presents the functions of such a hybrid system.

A system architecture for knowledge-based hypermedia

Hypermedia systems and knowledge systems can be viewed as flip-sides of the same coin. The former are designed to convey information and the latter to solve problems; developments beyond the basic techniques of each system type requires techniques from the other type. In this paper, we introduce the concept of knowledge-based or intelligent hypermedia and analyse various constellations of merged hypermedia and knowledge systems. A hypermedia system deals with informal and formalized theories and the relations between and within these. Therefore, the corner stones of our analysis are the very basic notions involved in formalizing domain knowledge: an informal domain theory, a formal object theory axiomatizing the informal theory and a metatheory analysing the properties and interrelations between and within these. We integrate these notions into a system architecture which is to serve as a programmable system schema for supporting the composition of actual intelligent hypermedia systems. Programming in the large is supported by the schema which defines the overall system structure whereas programming in the small is supported by knowledge modelling techniques. The application of the system architecture is illustrated by the construction of an interactive diagnosis system which involves knowledge-based reasoning both for navigation in hyperspace and problem-solving within the domain.

Generating Context-Based Explanations

Explanations are vital in knowledge-based systems. One problem, however, is that they ought to describe the domain context, not only the formal part utilised in the reasoning. A solution is thus to reproduce informal knowledge relating and complementing the formal one. The term “context-based explanations” is used for explanations based on formal and informal domain knowledge. An architecture generating such context-based explanations is described and appropriate knowledge to be presented is investigated.

Execution of Logic Programs

The time has come for us to find out how conclusions can be drawn from our world descriptions, i.e. how to execute Prolog programs. The conclusions are verified or refuted by a deduction. The clauses of the program are the premises of the deduction. A deduction consists of a sequence of deduction steps. To create a new step in the sequence we apply an inference rule either on premises or on formulas we have obtained from previous deduction steps. An inference rule states how a formula will follow from certain premises, i.e. an inference rule takes a set of premises and produces a conclusion:

Input and Output

\frac{{P_1 \, \cdots \,P_n }} {S}

Informal math coaching by instant messaging: Two case studies of how university students coach K-12 students

S is a conclusion inferred from the premises P1,…, P n .

Mechanization of an oracle in a debugging system

To enable deep learning from a system is not a trivial matter. By including domain context in the construction of the explanations the user is able to reflect on the effects of the knowledge, and not solely on the knowledge in isolation. In addition, in our approach the contextualized knowledge is to be viewed in the context of a particular goal or objective and how any reasoning result is reached in relation to this objective. In this paper we outline context-based explanations and their composition for a sample explanation supporting teachers in developing their own didactical skills.

Meeting the need for knowledge management in schools with knowledge-based systems – a case study

So far we have used the arguments in a question to give input to programs and to get output from programs. But we can also use built-in predicates for controlling the input and output of programs. In this way we can construct our own interface with the program.