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Neural Cognitive Diagnosis Model

NCDM in this package is the implementation of the NeuralCDM model in paper: Neural Cognitive Diagnosis for Intelligent Education Systems (also in paper NeuralCD: A General Cogntive Diagnosis Framework).

If this code helps with your studies, please kindly cite the following publication:

@article{wang2020neural,
  title={Neural Cognitive Diagnosis for Intelligent Education Systems},
  author={Wang, Fei and Liu, Qi and Chen, Enhong and Huang, Zhenya and Chen, Yuying and Yin, Yu and Huang, Zai and Wang, Shijin},
  booktitle={Thirty-Fourth AAAI Conference on Artificial Intelligence},
  year={2020}
}

or

@article{wang2022neuralcd,
  title={NeuralCD: A General Framework for Cognitive Diagnosis},
  author={Wang, Fei and Liu, Qi and Chen, Enhong and Huang, Zhenya and Yin, Yu and Wang, Shijin and Su, Yu},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}

Model description

NeuralCD is a neural-network-based general cognitive diagnosis framework, which uses neural network to learn the interactions among student abilities and exercise attributes from response data. NeuralCDM is a basic implementation of NeuralCD, where the neural network is multiple nonnegative full connections.

The model is implemented with Pytorch, and Adam optimizer is adopted for training.

Parameters description

Parameters Type Description
meta_data dict a dictionary containing all the userIds, itemIds, and skills.
hidd_dim1 int the dimension of the first hidden layer. Default: 512
hidd_dim2 int the dimension of the second hidden layer. Default: 256

Examples

import pandas as pd
from EduCDM import NCDM
meta_data = {'userId': ['001', '002', '003'], 'itemId': ['adf', 'w5'], 'skill': ['skill1', 'skill2', 'skill3', 'skill4']}
model = NCDM(meta_data, 512, 256)

train_data = pd.DataFrame({'userId':[1,1,2,2,3,3], 'itemId': [1,2,1,3,2,3], 'skill': ["[1]", "[1,3]", "[1]", "[1,2,3]", "[1,3]", "[1,2,3]"], 'response': [1,1,0,1,1,0]})
test_data = pd.DataFrame({'userId':[1,2,3], 'itemId': [3,2,1], 'skill': ["[1,2,3]", "[1,3]", "[1]"], 'response': [1,1,0]})
model.fit(train_data, epoch=2)
predict = model.predict(test_data)

Methods summary

Methods Description
fit Fits the model to the training data.
fit_predict Use the model to predict the responses in the testing data and returns the results. The responses are either 1 (i.e., correct answer) or 0 (i.e., incorrect answer).
fit_predict_proba Use the model to predict the responses in the testing data and returns the probabilities (that the correct answers will be provided).