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pykt-team / pykt-toolkit

pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
https://pykt.org
MIT License
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deep-learning dkt gkt knowledge-tracing knowledge-tracing-models

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pyKT

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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 and docs.

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
  18. https://github.com/lilstrawberry/ReKT

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
  25. ReKT: Revisiting Knowledge Tracing: A Simple and Powerful Model

Citation

We now have a paper you can cite for the our pyKT library:

@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}
}