A Survey on Recent Approaches to Question Difficulty Estimation from Text
Article Status
    Published
Authors/contributors
    - Benedetto, Luca (Author)
- Cremonesi, Paolo (Author)
- Caines, Andrew (Author)
- Buttery, Paula (Author)
- Cappelli, Andrea (Author)
- Giussani, Andrea (Author)
- Turrin, Roberto (Author)
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
DOI
    
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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