  The goal of this paper is to introduce a new framework for fast and effective
knowledge state assessments in the context of personalized, skill-based online
learning. We use knowledge state networks - specific neural networks trained on
assessment data of previous learners - to predict the full knowledge state of
other learners from only partial information about their skills. In combination
with a matching assessment strategy for asking discriminative questions we
demonstrate that our approach leads to a significant speed-up of the assessment
process - in terms of the necessary number of assessment questions - in
comparison to standard assessment designs. In practice, the presented methods
enable personalized, skill-based online learning also for skill ontologies of
very fine granularity without deteriorating the associated learning experience
by a lengthy assessment process.
