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Published in:
2015 | OriginalPaper | Chapter
Evaluating Human and Automated Generation of Distractors for Diagnostic Multiple-Choice Cloze Questions to Assess Children’s Reading Comprehension
Authors : Yi-Ting Huang, Jack Mostow
Published in: Artificial Intelligence in Education
Publisher: Springer International Publishing
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We report an experiment to evaluate DQGen’s performance in generating three types of distractors for diagnostic multiple-choice cloze (fill-in-the-blank) questions to assess children’s reading comprehension processes. Ungrammatical distractors test syntax, nonsensical distractors test semantics, and locally plausible distractors test inter-sentential processing. 27 knowledgeable humans rated candidate answers as correct, plausible, nonsensical, or ungrammatical without knowing their intended type or whether they were generated by DQGen, written by other humans, or correct. Surprisingly, DQGen did significantly better than humans at generating ungrammatical distractors and slightly better than them at generating nonsensical distractors, albeit worse at generating plausible distractors. Vetting its output and writing distractors only when necessary would take half as long as writing them all, and improve their quality.