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
Date
2023-09-30
Volume
55
Issue
9
Pages
1-37
Journal Abbr
ACM Comput. Surv.
DOI
Citation Key
benedetto2023
Accessed
08/10/2025, 23:06
ISSN
0360-0300, 1557-7341
Language
en
Library Catalogue
DOI.org (Crossref)
Extra
Read_Status: New
Read_Status_Date: 2026-01-26T11:33:20.824Z
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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