DIMKT

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.

Dependencies:

• python >= 3.7
• tesorflow-gpu >= 2.0
• numpy
• tqdm
• utils
• pandas
• sklearn

Usage

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}