pyKT is a python library build upon PyTorch to train deep learning based knowledge tracing models. The library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, 5 detailed prediction scenarios, 10 frequently compared DLKT approaches for transparent and extensive experiments.
Use the following command to install pyKT:
Create conda envirment.
conda create --name=pykt python=3.7.5
source activate pykt
pip install -U pykt-toolkit -i https://pypi.python.org/simple
- https://github.com/hcnoh/knowledge-tracing-collection-pytorch
- https://github.com/arshadshk/SAKT-pytorch
- https://github.com/shalini1194/SAKT
- https://github.com/arshadshk/SAINT-pytorch
- https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-
- https://github.com/arghosh/AKT
- https://github.com/JSLBen/Knowledge-Query-Network-for-Knowledge-Tracing
- https://github.com/xiaopengguo/ATKT
- https://github.com/jhljx/GKT
- https://github.com/THUwangcy/HawkesKT
- https://github.com/ApexEDM/iekt
- https://github.com/Badstu/CAKT_othermodels/blob/0c28d870c0d5cf52cc2da79225e372be47b5ea83/SKVMN/model.py
- https://github.com/bigdata-ustc/EduKTM
- DKT: Deep knowledge tracing
- DKT+: Addressing two problems in deep knowledge tracing via prediction-consistent regularization
- DKT-Forget: Augmenting knowledge tracing by considering forgetting behavior
- KQN: Knowledge query network for knowledge tracing: How knowledge interacts with skills
- DKVMN: Dynamic key-value memory networks for knowledge tracing
- ATKT: Enhancing Knowledge Tracing via Adversarial Training
- GKT: Graph-based knowledge tracing: modeling student proficiency using graph neural network
- SAKT: A self-attentive model for knowledge tracing
- SAINT: Towards an appropriate query, key, and value computation for knowledge tracing
- AKT: Context-aware attentive knowledge tracing
- Temporal Cross-Effects in Knowledge Tracing
- IEKT: Tracing Knowledge State with Individual Cognition and Acquisition Estimation
- SKVMN: Knowledge Tracing with Sequential Key-Value Memory Networks
- LPKT: Learning Process-consistent Knowledge Tracing
We now have a paper you can cite for the our pyKT library:
@article{liu2022pykt,
title={pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models},
author={Liu, Zitao and Liu, Qiongqiong and Chen, Jiahao and Huang, Shuyan and Tang, Jiliang and Luo, Weiqi},
journal={arXiv preprint arXiv:2206.11460},
year={2022}
}