A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
<|reference_start|>A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions: Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery. It has been applied to a wide range of fields including education, sport, psychological diagnosis, etc. By providing better awareness of cognitive status, it can serve as the basis for personalized services such as well-designed medical treatment, teaching strategy and vocational training. This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models. Further, we discuss future directions that are worthy of exploration. In addition, we release two Python libraries: EduData for easy access to some relevant public datasets we have collected, and EduCDM that implements popular CDMs to facilitate both applications and research purposes.<|reference_end|>
@article{wang2024a,
    title={A Survey of Models for Cognitive Diagnosis: New Developments and Future
  Directions},
    author={Fei Wang, Weibo Gao, Qi Liu, Jiatong Li, Guanhao Zhao, Zheng Zhang,
  Zhenya Huang, Mengxiao Zhu, Shijin Wang, Wei Tong, Enhong Chen},
    journal={arXiv preprint arXiv:2407.05458},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05458},
    primaryClass={cs.AI}
}