# Modeling of Item-Difficulty for Ontology-based MCQs

arXiv: https://arxiv.org/abs/1607.00869 · HTML: https://mlsanity.com/paper/1607.00869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [Evaluating the Fitness of Ontologies for the Task of Question Generation](https://mlsanity.com/paper/2504.07994/) (2025)

## Abstract (arXiv)

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.