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Found 3 papers, 2 papers with code
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text
Classical approaches to question calibration are either subjective or require newly created questions to be deployed before being calibrated.
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e. g., learning path customization, drop-out prediction).
R2DE: a NLP approach to estimating IRT parameters of newly generated questions
The main objective of exams consists in performing an assessment of students' expertise on a specific subject.
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