Seminar - Predicting the Difficulty of Multiple-Choice Questions in a High-stakes Medical Exam
|28 August 2019
|14:00 - 15:00
|What is it:
|Department of Computer Science
|Who is it for:
|University staff, Adults, Current University students
Join us for the next Computer Science Seminar with speaker Victoria Yaneva
Abstract: For many years, approaches from Natural Language Processing (NLP) have been applied to estimating reading difficulty, but relatively fewer attempts have been made to measure conceptual difficulty or question difficulty beyond linguistic complexity. In addition to expanding the horizons of NLP research, estimating the construct relevant difficulty of test questions has a high practical value because ensuring that exam questions are appropriately difficult is both one of the most important and one of the most costly tasks within the testing industry.
In this talk, I will present our ongoing experiments towards a method for estimating the difficulty of MCQs from a high-stakes medical exam, where all questions were deliberately written to a common reading level. To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system. Our results are compared to various baselines and the use of interpretable features allows drawing recommendations for item writing.
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