pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models
This work makes knowledge tracing more accessible to researchers and practitioners, but it is incremental as it repackages existing methods into a library.
The authors introduced pyBKT, an accessible Python library for Bayesian Knowledge Tracing models, providing efficient implementations and tools for data handling, and validated its runtime and accuracy against past implementations and real-world data.
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.