Adaptive assessment using granularity hierarchies and bayesian nets

Abstract

Adaptive testing is impractical in real world situations where many different learner traits need to be measured in a single test. Recent student modelling approaches have attempted to solve this problem using different course representations along with sound knowledge propagation schemes. This paper shows that these different representations can be merged together and realized in a granularity hierarchy. Bayesian inference can be used to propagate knowledge throughout the hierarchy. This provides information for selecting appropriate test items and maintains a measure of the student’s knowledge level.