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.

借助人工智能（AI）的进步和应用，个性化教育可能成为未来新教育系统的基石。本研究提供了 PEEKC 数据集和 TrueLearn Python 库，其中包含一系列在线学习者状态模型，对促进学习者参与建模至关重要。TrueLearn 模型系列是根据 “开放式学习者” 概念设计的，使用直观的用户表达方式。这个可扩展的在线模型系列还可以帮助最终用户可视化学习者模型，这可能在未来促进用户与他们的模型 / 推荐者之间的互动。丰富的文档和编码示例使得该库对于机器学习开发人员和教育数据挖掘和学习分析专业人员都非常易于使用。实验结果显示，该数据集和库的效用显著超过了比较基准模型。数据集包含大量与人工智能相关的教育视频，对于构建和验证面向 AI 的教育推荐系统具有重要意义。

教育视频参与度建模工具箱

A Toolbox for Modelling Engagement with Educational Videos

Educational recommenders have received much less attention in comparison to
e-commerce and entertainment-related recommenders, even though efficient
intelligent tutors have great potential to improve learning gains. One of the
main challenges in advancing this research direction is the scarcity of large,
publicly available datasets. In this work, we release a large, novel dataset of
learners engaging with educational videos in-the-wild. The dataset, named
Personalised Educational Engagement with Knowledge Topics PEEK, is the first
publicly available dataset of this nature. The video lectures have been
associated with Wikipedia concepts related to the material of the lecture, thus
providing a humanly intuitive taxonomy. We believe that granular learner
engagement signals in unison with rich content representations will pave the
way to building powerful personalization algorithms that will revolutionise
educational and informational recommendation systems. Towards this goal, we 1)
construct a novel dataset from a popular video lecture repository, 2) identify
a set of benchmark algorithms to model engagement, and 3) run extensive
experimentation on the PEEK dataset to demonstrate its value. Our experiments
with the dataset show promise in building powerful informational recommender
systems. The dataset and the support code is available publicly.

本文介绍推荐系统中教育推荐的研究现状和挑战，并放出了一个名为 PEEK 的数据集，为教育和信息推荐系统的个性化算法建设提供基础。