Difficulty-level Modeling of Ontology-based Factual Questions
  Semantics based knowledge representations such as ontologies are found to be
very useful in automatically generating meaningful factual questions.
Determining the difficulty level of these system generated questions is helpful
to effectively utilize them in various educational and professional
applications. The existing approaches for finding the difficulty level of
factual questions are very simple and are limited to a few basic principles. We
propose a new methodology for this problem by considering an educational theory
called Item Response Theory (IRT). In the IRT, knowledge proficiency of end
users (learners) are considered for assigning difficulty levels, because of the
assumptions that a given question is perceived differently by learners of
various proficiencies. We have done a detailed study on the features (factors)
of a question statement which could possibly determine its difficulty level for
three learner categories (experts, intermediates and beginners). We formulate
ontology based metrics for the same. We then train three logistic regression
models to predict the difficulty level corresponding to the three learner
categories.
