This paper describes a strategy for evaluating an assessor algorithm through the use of simulated students. The assessor is an adaptive testing strategy elicited from a human expert. The assessor claims an accurate assessment of the student's domain knowledge with a minimum number of questions presented to the student. An evaluation of the assessor is carried out to test the claim. Three types of students are simulated-one who knows all the relevant skills in the subject domain, one who knows a subset of the relevant skills, and one who has one or more malrules. The performance of the assessor in predicting the mastery of skills of each student is analysed. Future work involves the evaluation of its accuracy in the assessment of other types of students, such as students with lucky guesses or careless slips.

A power electronic system in which a constant source supplies a load through an input filter and a regulated DC-DC converter is considered. Results from the circuit theory literature are applied to yield conditions on the network parameters ensuring complete stability (i.e., convergence of all trajectories to an equilibrium point) of the dynamic model of the system. It is also shown that if one of the conditions does not apply, then the model undergoes an Andronov-Hopf bifurcation to unstable limit cycles for some parameter values.

In this paper it is argued that an expert system requires more than factual knowledge before it can display expertise in a given domain. The additional knowledge consists of the heuristics or 'rules of thumb' used by an expert to manipulate and interpret the factual knowledge. The knowledge acquisition phase of an expert system project involves determining the factual knowledge (which may be obtained from published sources) and the heuristics used by an expert to manipulate that knowledge--these heuristics can only be obtained from an expert. In reviewing existing biological expert systems it is apparent that many contain only the factual knowledge relating to the domain, and lack the heuristics that enable such systems to show expertise. This paper reviews a number of knowledge acquisition techniques which could be used for acquiring heuristic knowledge and discusses when their use is appropriate. The knowledge acquisition techniques discussed are those suitable for the development of small-scale expert systems as these are most likely to be of interest to biologists. The techniques include the use of questionnaires, interview techniques and protocol analysis; particular emphasis is placed on a modification to the 'twenty questions' interview technique which was developed specifically to elicit taxonomic knowledge relating to water mite identification.