The use of constraint logic programming in the development of adaptive tests

This research shows how Constraint Logic Programming can form the basis of a knowledge elicitation tool for the development of adaptive tests. A review of literature reveals that adaptive testing has, in recent years, been used as a student modelling tool in intelligent tutoring systems. Efforts in the construction and delivery of such tests have involved the use of state-space type constructs, such as granularity hierarchies and knowledge spaces, to represent a syllabus. The use of Bayesian probability networks has been proposed as a technique for finding an optimal route through these ”spaces” so as to find the shortest sequence of problems to put to the student being evaluated. The research presented here sets out to use ”expert emulation” as means of performing the same tasks. The aim is to construct an adaptive test in order to model a student’s knowledge, skills and tutorial requirements. The context is the development of such a system to support autonomous revision for examination in a particular domain in elementary mathematics.