A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
It provides a comprehensive review and tools for researchers and practitioners in fields like education and psychology, but it is incremental as it synthesizes existing work.
This paper surveys current models for cognitive diagnosis, focusing on new developments using machine learning-based methods, and releases two Python libraries (EduData and EduCDM) to facilitate access to datasets and implementation of models.
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.