pyKT

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

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. 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

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}
}