Source code and data set for the paper Neural Cognitive Diagnosis for Intelligent Education Systems and NeuralCD: A General Cogntive Diagnosis Framework.

The code in this repository is the implementation of NeuralCDM model, and the data set is the public data set ASSIST2009-2010.

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}
}

For the implementation of NeuralCDM+, please refer to https://github.com/LegionKing/NeuralCDM_plus .

For the implementation of KaNCD (in "NeuralCD: A General Cogntive Diagnosis Framework") and other typical cognitive diagnosis models, please refer to our github repository: https://github.com/bigdata-ustc/EduCDM .

• python 3.6
• pytorch >= 1.0 (pytorch 0.4 might be OK but pytorch<0.4 is not applicable)
• numpy
• json
• sklearn

Run divide_data.py to divide the original data set data/log_data.json into train set, validation set and test set. The data/ folder has already contained divided data so this step can be skipped.

python divide_data.py

Train the model:

python train.py {device} {epoch}

For example:

python train.py cuda:0 5 or python train.py cpu 5

Test the trained the model on the test set:

python predict.py {epoch}

The data/log_data.json is extracted from public data set ASSIST2009-2010 (skill-builder, corrected version) where answer_type != 'open_response' and skill_id != ''. When a user answers a problem for multiple times, only the first time is kept. The logs are organized in the structure:

• log_data.json = [user1, user2, ...]
• user = {"user_id": user_id, "log_num": log_num, "logs": [log1, log2, ...]]}
• log = {"exer_id": exer_id, "score": score, "knowledge_code": [knowledge_code1, knowledge_code1, ...]}

The user_id, exer_id and knowledge_code correspond to user_id, problem_id and skill_id attributes in the original ASSIST2009-2010 csv file. The ids/codes are recoded starting from 1.

• Students with less than 15 logs would be deleted in divide_data.py.
• The model parameters are initialized with Xavier initialization.
• The model uses Adam Optimizer, and the learning rate is set to 0.002.

There is a mistake in the AAAI conference paper. Eq. (18) should be: