1. Review of Question Difficulty Evaluation Approaches.
- Author
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XU Jia, WEI Tingting, YU Ge, HUANG Xinyue, and LYU Pin
- Subjects
DATA mining ,DEEP learning ,INTELLIGENT tutoring systems - Abstract
Difficulty of a question is not only the key information to ensure the rationality of a test paper and the fairness of a test, but also acts as a critical parameter in intelligent tutoring system (ITS), effectively supporting many intelligent teaching functions, such as intelligent paper forming, automatic question generation, and personalized exercise recommendation. Therefore, question difficulty evaluation has become an important research direction in the field of educational data mining, and has a lot of research work. This paper comprehensively reviews the research progress of question difficulty evaluation in recent ten years, divides the question difficulty into two categories: absolute difficulty and relative difficulty, sorts out and classifies exsiting evaluation approaches of question difficulty, and mainly explains deep learning based approaches for both question absolute difficulty prediction and question relative difficulty prediction. Specifically, important approaches of deep learning based question relative difficulty prediction are experimentally analyzed. Meanwhile, related datasets and evaluation metrics of question difficulty prediction approaches are summarized. Finally, the future research directions of question difficulty evaluation are prospected. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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