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Disentangled Knowledge Tracing for Alleviating Cognitive Bias

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# 🔥DisKT: Disentangled Knowledge Tracing for Alleviating Cognitive Bias (WWW2025)

PyTorch implementation of [DisKT](https://openreview.net/forum?id=z13UqeDT39)

🌹**Many thanks to Ringotc and nguyendinhhieu12071988-afk for pointing out the data leak issue in our code and providing a fix.**

[![arXiv](https://img.shields.io/badge/Arxiv-2503.02539-red?logo=arxiv&label=Arxiv&color=red)](https://arxiv.org/abs/2503.02539)
[![License](https://img.shields.io/badge/Code%20License-MIT%20License-yellow)](https://github.com/zyy-2001/DisKT/blob/master/LICENSE)
![GitHub Repo stars](https://img.shields.io/github/stars/zyy-2001/DisKT)

</h5>

## 🌟Data and Data Preprocessing

Place the [assist09](https://sites.google.com/view/assistmentsdatamining/dataset?authuser=0), [algebra05, algebra06](https://pslcdatashop.web.cmu.edu/KDDCup), [statics](https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=507), [ednet](https://github.com/riiid/ednet), [prob, comp, linux, database](https://github.com/wahr0411/PTADisc), [spanish](https://github.com/robert-lindsey/WCRP), and [slepemapy](https://www.fi.muni.cz/adaptivelearning/?a=data) source files in the dataset directory, and process the data using the following commands respectively:

```python
python preprocess_data.py --data_name assistments09
python preprocess_data.py --data_name [algebra05, bridge_algebra06]
python preprocess_data.py --data_name statics
python preprocess_data.py --data_name ednet
python preprocess_data.py --data_name [prob, sampled_comp, linux, database]
python preprocess_data.py --data_name spanish
python preprocess_data.py --data_name sampled_slepemapy
```

The statistics of the 11 datasets after processing are as follows:

| Datasets  | #students | #questions | #concepts | #concepts* | #interactions |
| --------- | --------- | ---------- | --------- | ---------- | ------------- |
| assist09  | 3,644     | 17,727     | 123       | 150        | 281,890       |
| algebra05 | 571       | 173,113    | 112       | 271        | 607,014       |
| algebra06 | 1,138     | 129,263    | 493       | 550        | 1,817,450     |
| statics   | 333       | 1,223      | N/A       | N/A        | 189,297       |
| ednet     | 5,000     | 12,117     | 189       | 1,769      | 676,276       |
| prob      | 512       | 1,054      | 247       | 247        | 42,869        |
| comp      | 5,000     | 7,460      | 445       | 445        | 668,927       |
| linux     | 4,375     | 2,672      | 281       | 281        | 365,027       |
| database  | 5,488     | 3,388      | 291       | 291        | 990,468       |
| spanish   | 182       | 409        | 221       | 221        | 578,726       |
| slepemapy | 5,000     | 2,723      | 1,391     | 1,391      | 625,523       |

**Table1: Statistics of 11 datasets. "#concepts\*" denotes the total number of concepts after converting multiple concepts into a new concept.**

The dataset processed with PTADisc can be found at the [link](https://drive.google.com/file/d/1IFys5t9J2yzOz_KLk86EglnBU2bftEJG/view?usp=sharing).

## ➡️Quick Start

### Installation

Git clone this repository and create conda environment:

```python
conda create -n diskt python=3.10.13
conda activate diskt
pip install -r requirements.txt 
```

Specially, Mamba requires a different CUDA version, please strictly follow the installation instructions for [Mamba](https://github.com/state-spaces/mamba) as provided in its respective GitHub repository. Downloading the correct CUDA packages is crucial.

### Training & Testing

Our model experiments are conducted on two NVIDIA RTX 3090 24GB GPUs. You can execute it directly using the following commands:

```python
CUDA_VISIBLE_DEVICES=0 python main.py --model_name [diskt, dkt, dkvmn, skvmn, deep_irt, gkt, sakt, akt, atkt, cl4kt, corekt, dtransformer, simplekt, folibikt, sparsekt, mikt] --data_name [assist09, algebra05, algebra06, statics, ednet, prob, sampled_comp, linux ,database, spanish, sampled_slepemapy]
```

## 🎈Citation
If you find our work valuable, we would appreciate your citation: 
```text
@inproceedings{zhou2025disentangled,
  title={Disentangled Knowledge Tracing for Alleviating Cognitive Bias},
  author={Zhou, Yiyun and Lv, Zheqi and Zhang, Shengyu and Chen, Jingyuan},
  booktitle={Proceedings of the ACM on Web Conference 2025},
  pages={2633--2645},
  year={2025}
}
```

DisKT

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