This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.

翻译：本文介绍了TrueLearn Python库，该库包含一系列用于构建教育（或更广义的信息）推荐系统的在线学习贝叶斯模型。这类模型遵循"开放学习者"概念设计，采用具有人类直觉性的用户表征。为提升可解释性并赋予用户自主权，TrueLearn库还提供多种可视化表征，帮助最终用户理解学习者模型，未来可能促进用户与其自身模型的交互。随库发布的还有此前公开的隐式反馈教育数据集及评估模型性能的指标体系。详尽的文档和编程示例使该库对机器学习开发者、教育数据挖掘及学习分析从业者均具有高度可访问性。该库及其配套示例文档可从https://truelearn.readthedocs.io/en/latest 获取。