EduQG: A Multi-Format Multiple-Choice Dataset for the Educational Domain
نویسندگان
چکیده
Natural language processing technology has made significant progress in recent years, fuelled by increasingly powerful general models. This also inspired a sizeable body of work targeted specifically towards the educational domain, where creation questions (both for assessment and practice) is laborious/expensive effort. Thus, automatic Question-Generation (QG) solutions have been proposed studied. Yet, according to survey QG community’s progress, common baseline dataset unifying multiple domains question forms (e.g., choice vs. fill-the-gap), including readily available models compare against, largely missing. gap we aim fill with this paper. In particular, introduce high-quality containing over 3,000 entries, comprising (i) multiple-choice questions, (ii) corresponding answers (including distractors), (iii) associated passages from course material used as sources questions. Each phrased two forms, normal cloze (i.e., correct are linked source documents sentence-level annotations. our versatile can be both distractor generation, well explore new challenges such format conversion. Furthermore, 903 accompanied their cognitive complexity level per Bloom’s taxonomy. All generated experts rather than crowd workers ensure they maintaining learning standards. Our analysis experiments suggest distinguishable differences between commonly ones generation purposes. We believe serve valuable resource research evaluation domain. The baselines support further education (https://github.com/hadif ar/question-generation).
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3248790