Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches.

中文翻译：

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pyBKT：贝叶斯知识跟踪模型的可访问Python库

贝叶斯知识追踪（Bayesian Knowledge Tracing）是一种用于认知掌握估计的模型，一直是自适应学习研究的标志，也是已部署的智能辅导系统（ITS）的组成部分。在本文中，我们提供了有关知识跟踪模型研究的简要历史，并介绍了pyBKT，这是一种从文献中可以访问并且计算效率高的模型扩展库。该库提供数据生成，拟合，预测和交叉验证例程，以及易于使用的数据帮助程序界面来提取典型的导师日志数据集格式。我们使用各种数据集大小评估运行时，并与过去的实现进行比较。此外，我们使用实验和模拟数据对模型进行完整性检查，以评估其EM参数学习的准确性，并使用实际数据验证其预测，比较pyBKT支持的模型变体与最初引入它们的论文的结果。该图书馆是开放源代码和开放许可，其目的是使研究和实践社区更容易获得知识追踪，并通过更容易地复制过去的方法来促进该领域的进步。