Advertisement
Published in:
2015 | OriginalPaper | Chapter
Distractor Quality Evaluation in Multiple Choice Questions
Authors : Van-Minh Pho, Anne-Laure Ligozat, Brigitte Grau
Published in: Artificial Intelligence in Education
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Multiple choice questions represent a widely used evaluation mode; yet writing items that properly evaluate student learning is a complex task. Guidelines were developed for manual item creation, but automatic item quality evaluation would constitute a helpful tool for teachers.
In this paper, we present a method for evaluating distractor (i.e. incorrect option) quality that combines syntactic and semantic homogeneity criteria, based on Natural Language Processing methods. We perform an evaluation of this method on a large MCQ corpus and show that the combination of several measures enables us to validate distractors.