# DIMKT

**Repository Path**: abc-pedicle/DIMKT

## Basic Information

- **Project Name**: DIMKT
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-04
- **Last Updated**: 2023-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-withaffect).



## 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](https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-withaffect), 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}`