The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM) and Item Response Ranking framework (IRR).
The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM), Item Response Ranking framework (IRR), Incremental Cognitive Diagnosis (ICD) and Knowledge-association baesd extension of NeuralCD (KaNCD).
Cognitive diagnosis model (CDM) for intelligent educational systems is a type of model that infers students' knowledge states from their learning behaviors (especially exercise response logs).
Typically, the input of a CDM could be the students' response logs of items (i.e., exercises/questions), the Q-matrix that denotes the correlation between items and knowledge concepts (skills). The output is the diagnosed student knowledge states, such as students' abilities and students' proficiencies on each knowledge concepts.
Traditional CDMs include:
etc.
More recent researches about CDMs:
Git and install with pip
:
git clone https://github.com/bigdata-ustc/EduCDM.git
cd path/to/code
pip install .
Or directly install from pypi:
pip install EduCDM
EduCDM is still under development. More algorithms and features are going to be added and we always welcome contributions to help make EduCDM better. If you would like to contribute, please follow this guideline.
If this repository is helpful for you, please cite our work
@misc{bigdata2021educdm,
title={EduCDM},
author={bigdata-ustc},
publisher = {GitHub},
journal = {GitHub repository},
year = {2021},
howpublished = {\url{https://github.com/bigdata-ustc/EduCDM}},
}
[1] Liu Q, Wu R, Chen E, et al. Fuzzy cognitive diagnosis for modelling examinee performance[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2018, 9(4): 1-26.
[2] Wang F, Liu Q, Chen E, et al. Neural cognitive diagnosis for intelligent education systems[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(04): 6153-6161.
[3] Tong S, Liu Q, Yu R, et al. Item response ranking for cognitive diagnosis[C]. IJCAI, 2021.
[4] Wang F, Liu Q, Chen E, et al. NeuralCD: A General Framework for Cognitive Diagnosis. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted, 2022.