A Survey on Recent Approaches to Question Difficulty Estimation from Text

Article Status
Published
Authors/contributors
Title
A Survey on Recent Approaches to Question Difficulty Estimation from Text
Abstract
Question Difficulty Estimation from Text (QDET) is the application of Natural Language Processing techniques to the estimation of a value, either numerical or categorical, which represents the difficulty of questions in educational settings. We give an introduction to the field, build a taxonomy based on question characteristics, and present the various approaches that have been proposed in recent years, outlining opportunities for further research. This survey provides an introduction for researchers and practitioners into the domain of question difficulty estimation from text and acts as a point of reference about recent research in this topic to date.
Publication
ACM Computing Surveys
Volume
55
Issue
9
Pages
1-37
Date
2023-09-30
Journal Abbr
ACM Comput. Surv.
Language
en
ISSN
0360-0300, 1557-7341
Accessed
08/10/2025, 23:06
Library Catalogue
DOI.org (Crossref)
Extra
Citation Key: benedetto2023
Citation
Benedetto, L., Cremonesi, P., Caines, A., Buttery, P., Cappelli, A., Giussani, A., & Turrin, R. (2023). A Survey on Recent Approaches to Question Difficulty Estimation from Text. ACM Computing Surveys, 55(9), 1–37. https://doi.org/10.1145/3556538
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