Use this URL to cite or link to this record in EThOS:
Title: Algorithms for assessing the quality and difficulty of multiple choice exam questions
Author: Luger, Sarah Kaitlin Kelly
ISNI:       0000 0004 6063 1517
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Multiple Choice Questions (MCQs) have long been the backbone of standardized testing in academia and industry. Correspondingly, there is a constant need for the authors of MCQs to write and refine new questions for new versions of standardized tests as well as to support measuring performance in the emerging massive open online courses, (MOOCs). Research that explores what makes a question difficult, or what questions distinguish higher-performing students from lower-performing students can aid in the creation of the next generation of teaching and evaluation tools. In the automated MCQ answering component of this thesis, algorithms query for definitions of scientific terms, process the returned web results, and compare the returned definitions to the original definition in the MCQ. This automated method for answering questions is then augmented with a model, based on human performance data from crowdsourced question sets, for analysis of question difficulty as well as the discrimination power of the non-answer alternatives. The crowdsourced question sets come from PeerWise, an open source online college-level question authoring and answering environment. The goal of this research is to create an automated method to both answer and assesses the difficulty of multiple choice inverse definition questions in the domain of introductory biology. The results of this work suggest that human-authored question banks provide useful data for building gold standard human performance models. The methodology for building these performance models has value in other domains that test the difficulty of questions and the quality of the exam takers.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Item Response Theory ; IRT ; Multiple Choice Questions ; MCQs ; question answering ; question difficulty ; question discrimination