With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models that are essential to facilitate research on learner engagement modelling.TrueLearn family of models was designed following the "open learner" concept, using humanly-intuitive user representations. This family of scalable, online models also help end-users visualise the learner models, which may in the future facilitate user interaction with their models/recommenders. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytics practitioners. The experiments show the utility of both the dataset and the library with predictive performance significantly exceeding comparative baseline models. The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.

翻译：随着人工智能（AI）的发展及应用，为全球人口提供个性化教育可能成为未来新型教育体系的基石。本文介绍了PEEKC数据集及TrueLearn Python库，其中包含一个数据集和一系列在线学习者状态模型，这些模型对于促进学习者参与度建模研究至关重要。TrueLearn模型系列遵循"开放学习者"概念设计，采用人性化的用户表征方式。这一系列可扩展的在线模型还能帮助终端用户可视化学习者模型，未来可能促进用户与其模型/推荐系统之间的交互。详尽的文档和代码示例使该库对于机器学习开发者、教育数据挖掘以及学习分析从业者均具有高度可访问性。实验表明该数据集与库具有实用价值，其预测性能显著优于对比基准模型。该数据集包含大量与AI相关的教育视频，对于构建和验证专门面向AI的教育推荐系统具有重要意义。