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pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
| Date | Stars |
|---|---|
| 2026-07-31 | 423 |
| 2026-08-02 | 423 |
| 2026-08-06 | 425 |
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# 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 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. stab
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