Predicting Item Response Theory Parameters Using Question Statements Texts
Article Status
Published
Authors/contributors
- Marinho, Wemerson (Author)
- Clua, Esteban Walter (Author)
- Martí, Luis (Author)
- Marinho, Karla (Author)
Title
Predicting Item Response Theory Parameters Using Question Statements Texts
Proceedings Title
LAK23: 13th International Learning Analytics and Knowledge Conference
Conference Name
LAK 2023: 13th International Learning Analytics and Knowledge Conference
Publisher
ACM
Place
Arlington TX USA
Date
2023-3-13
Pages
1-10
ISBN
978-1-4503-9865-7
Citation Key
marinho2023
Accessed
13/12/2023, 18:08
Language
en
Library Catalogue
DOI.org (Crossref)
Extra
<标题>: 使用问题陈述文本预测项目反应理论参数
<AI Smry>: The application of new Neural Language Models pre-trained on a massive corpus of texts to predict Item Response Theory parameters in multiple choice questions for the Brazilian National High School Exam (ENEM) is investigated and a novel optimization target for regression is proposed: Item Characteristic Curve.
Read_Status: New
Read_Status_Date: 2026-01-26T11:33:34.519Z
Citation
Marinho, W., Clua, E. W., Martí, L., & Marinho, K. (2023). Predicting Item Response Theory Parameters Using Question Statements Texts. LAK23: 13th International Learning Analytics and Knowledge Conference, 1–10. https://doi.org/10.1145/3576050.3576139
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