Modeling of Item-Difficulty for Ontology-based MCQs
  Multiple choice questions (MCQs) that can be generated from a domain ontology
can significantly reduce human effort & time required for authoring &
administering assessments in an e-Learning environment. Even though here are
various methods for generating MCQs from ontologies, methods for determining
the difficulty-levels of such MCQs are less explored. In this paper, we study
various aspects and factors that are involved in determining the
difficulty-score of an MCQ, and propose an ontology-based model for the
prediction. This model characterizes the difficulty values associated with the
stem and choice set of the MCQs, and describes a measure which combines both
the scores. Further more, the notion of assigning difficultly-scores based on
the skill level of the test taker is utilized for predicating difficulty-score
of a stem. We studied the effectiveness of the predicted difficulty-scores with
the help of a psychometric model from the Item Response Theory, by involving
real-students and domain experts. Our results show that, the predicated
difficulty-levels of the MCQs are having high correlation with their actual
difficulty-levels.
