Source code and data set for our paper (recently accepted in SIGIR2022): Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect.
The code is the implementation of DIMKT model, and the data set is the public data set ASSIST2012-2013.
- python >= 3.7
- tesorflow-gpu >= 2.0
- numpy
- tqdm
- utils
- pandas
- sklearn
First, download the data file: 2012-2013-data-with-predictions-4-final.csv, then put it in the folder 'data/'
Then, run data_pre.py to preprocess the data set, and run data_save.py {sequence length} to divide the original data set into train set, validation set and test set.
python data_pre.py
python data_save.py 100
Train the model:
python train.py {fold}
For example:
python train.py 1 or python train.py 2
Test the trained the model on the test set:
python test.py {model_name}