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  • pyKT Team
    pyKT is a python library build upon PyTorch to train deep learning based knowledge tracing models Easy to Use We carefully design each module to make the codes friendly for newcomers Only three steps are needed to run a model And each module is well developed to resued, which makes building new models and creating new datasets easier
  • What is 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 7 popular datasets across different domains, 5 detailed prediction scenarios, 10 frequently compared DLKT approaches for transparent and extensive experiments
  • QIKT: Question-centric Interpretable Knowledge Tracing - pyKT
    We added QIKT into our pyKT package The link is here and the API is here Original paper can be found at Chen, Jiahao, et al “Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations ” arXiv preprint arXiv:2302 06885 (2023) Title: Improving interpretability of deep sequential knowledge tracing models with question-centric
  • Dtransformer: Stable Knowledge Tracing with Diagnostic Transformer - pyKT
    We added Dtransformer into our pyKT package The link is here Original paper can be found at Yin, Yu, et al “Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer ” Proceedings of the ACM Web Conference 2023 Title: Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer Abstract:Knowledge Tracing (KT) aims at
  • DIMKT: DIfficulty Matching Knowledge Tracing - pyKT
    We added DIMKT into our pyKT package The link is here and the API is here Original paper can be found at Shen, Shuanghong, et al “Assessing Student’s Dynamic Knowledge State by Exploring the Question Difficulty Effect ” Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval 2022 Title: Assessing Student’s Dynamic Knowledge
  • Welcome to PyKT’s documentation! — pykt-toolkit 0. 0. 32 documentation
    Welcome to PyKT’s documentation! 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 Let’s Get
  • HCGKT: Hierarchical Contrastive Graph Knowledge Tracing with Multi . . .
    We added HCGKT into our pyKT package The link is here and the API is here Original paper can be found at ZhiW,Huang et al “HCGKT: Hierarchical Contrastive Graph Knowledge Tracing with Multi-level Feature Learning ” International Conference on Artificial Intelligence in Education 2025 Title: HCGKT: Hierarchical Contrastive Graph Knowledge Tracing with Multi-level Feature Learning
  • pykt. datasets package — pykt-toolkit 0. 0. 32 documentation
    pykt datasets package Submodules pykt datasets data_loader module class pykt datasets data_loader KTDataset(file_path, input_type, folds, qtest=False) [source] Bases: torch utils data dataset Dataset Dataset for KT can use to init dataset for: (for models except dkt_forget) train data, valid data common test data (concept level evaluation), real educational scenario test data (question level
  • New dataset ASSISTments2012 - pyKT
    The “ASSISTments2012” dataset is a comprehensive collection of educational data from the 2012-2013 school year, with affect predictions The dataset consists of 2,541,201 interactions, 27,066 students, and 45,716 questions, including information on student interactions with an intelligent tutoring system and is a product of the work of Ryan Baker and his colleagues The dataset has been
  • pykt-toolkit — pykt-toolkit 0. 0. 32 documentation
    pykt-toolkit 现有的知识追踪（Knowledge Tracing）工作中缺乏一个统一的数据划分标准，导致在各个知识追踪文献中，即便是相同的数据，由于划分方式不同，导致在相同的模型上也有不同的实验结果。因此，本工作旨在构建一个统一的数据预处理及划分结果，并在多个知识追踪SOTA上调优，供后续的知识





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