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https://github.com/pykt-team/pykt-toolkit
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
https://github.com/pykt-team/pykt-toolkit
deep-learning dkt gkt knowledge-tracing knowledge-tracing-models
Last synced: about 2 months ago
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pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
- Host: GitHub
- URL: https://github.com/pykt-team/pykt-toolkit
- Owner: pykt-team
- License: mit
- Created: 2022-05-14T02:04:29.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-15T10:15:35.000Z (9 months ago)
- Last Synced: 2024-03-15T11:30:05.678Z (9 months ago)
- Topics: deep-learning, dkt, gkt, knowledge-tracing, knowledge-tracing-models
- Language: Python
- Homepage: https://pykt.org
- Size: 35.7 MB
- Stars: 167
- Watchers: 7
- Forks: 45
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pyKT
[](https://pepy.tech/project/pykt-toolkit)
[](https://github.com/pykt-team/pykt-toolkit/issues)
[](https://pykt.org/)
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 more than 7 popular datasets across different domains, 5 detailed prediction scenarios, more than 10 frequently compared DLKT approaches for transparent and extensive experiments. More details about pyKT can see our [website](https://pykt.org/) and [docs](https://pykt-toolkit.readthedocs.io/en/latest/quick_start.html).
## Installation
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
```
## Hyper parameter tunning results
The hyper parameter tunning results of our experiments about all the DLKT models on various datasets can be found at https://drive.google.com/drive/folders/1MWYXj73Ke3zC6bm3enu1gxQQKAHb37hz?usp=drive_link.
## References
### Projects
1. https://github.com/hcnoh/knowledge-tracing-collection-pytorch
2. https://github.com/arshadshk/SAKT-pytorch
3. https://github.com/shalini1194/SAKT
4. https://github.com/arshadshk/SAINT-pytorch
5. https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-
6. https://github.com/arghosh/AKT
7. https://github.com/JSLBen/Knowledge-Query-Network-for-Knowledge-Tracing
8. https://github.com/xiaopengguo/ATKT
9. https://github.com/jhljx/GKT
10. https://github.com/THUwangcy/HawkesKT
11. https://github.com/ApexEDM/iekt
12. https://github.com/Badstu/CAKT_othermodels/blob/0c28d870c0d5cf52cc2da79225e372be47b5ea83/SKVMN/model.py
13. https://github.com/bigdata-ustc/EduKTM
14. https://github.com/shalini1194/RKT
15. https://github.com/shshen-closer/DIMKT
16. https://github.com/skewondr/FoLiBi
17. https://github.com/yxonic/DTransformer
### Papers
1. DKT: Deep knowledge tracing
2. DKT+: Addressing two problems in deep knowledge tracing via prediction-consistent regularization
3. DKT-Forget: Augmenting knowledge tracing by considering forgetting behavior
4. KQN: Knowledge query network for knowledge tracing: How knowledge interacts with skills
5. DKVMN: Dynamic key-value memory networks for knowledge tracing
6. ATKT: Enhancing Knowledge Tracing via Adversarial Training
7. GKT: Graph-based knowledge tracing: modeling student proficiency using graph neural network
8. SAKT: A self-attentive model for knowledge tracing
9. SAINT: Towards an appropriate query, key, and value computation for knowledge tracing
10. AKT: Context-aware attentive knowledge tracing
11. HawkesKT: Temporal Cross-Effects in Knowledge Tracing
12. IEKT: Tracing Knowledge State with Individual Cognition and Acquisition Estimation
13. SKVMN: Knowledge Tracing with Sequential Key-Value Memory Networks
14. LPKT: Learning Process-consistent Knowledge Tracing
15. QIKT: Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations
16. RKT: Relation-aware Self-attention for Knowledge Tracing
17. DIMKT: Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect
18. ATDKT: Enhancing Deep Knowledge Tracing with Auxiliary Tasks
19. simpleKT: A Simple but Tough-to-beat Baseline for Knowledge Tracing
20. SparseKT: Towards Robust Knowledge Tracing Models via K-sparse Attention
21. FoLiBiKT: Forgetting-aware Linear Bias for Attentive Knowledge Tracing
22. DTransformer: Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer
23. stableKT: Enhancing Length Generalization for Attention Based Knowledge Tracing Models with Linear Biases
24. extraKT: Extending Context Window of Attention Based Knowledge Tracing Models via Length Extrapolation
## Citation
We now have a [paper](https://arxiv.org/abs/2206.11460?context=cs.CY) you can cite for the our pyKT library:
```bibtex
@inproceedings{liupykt2022,
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},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022}
}
```