EdNet：一个大规模的教育层次数据集
EdNet: A Large-Scale Hierarchical Dataset in Education
摘要：
随着人工智能在教育领域的发展和交互式教育系统规模的不断扩大，数据驱动的方法已经成为知识追踪和学习路径推荐等各种任务的常用方法。遗憾的是，收集真实的学生互动数据往往是具有挑战性的，这导致缺乏公共的大规模基准数据集，反映各种各样的学生行为在现代IES。虽然ASSISTments、Junyi Academy、Synthetic和STATICS等数据集是公开的，并被广泛使用，但它们的规模还不足以充分发挥最先进的数据驱动模型的潜力，并将记录的行为限制在解决问题的活动上。为此，我们引入了EdNet，这是Santa收集的多样化学生活动的大规模分层数据集，这是一个配备人工智能辅导系统的多平台自学解决方案。EdNet包含了两年多来收集的784，309名学生的131，441，538次互动，这是迄今为止向公众发布的ITS数据集中最大的。与现有的数据集不同，EdNet提供了各种各样的学生行为，从解决问题到讲座消费和物品购买。此外，EdNet有一个层次结构，学生的行为被分为4个不同的抽象层次。EdNet的特性是领域无关的，允许EdNet轻松扩展到不同的领域。该数据集在Creative Commons Attribution-NonCommercial 4.0 International许可下公开发布，用于研究目的。我们计划通过EdNet在多个AIEd任务中发起挑战，为不同的最先进模型之间的公平比较提供共同基础，并鼓励开发实用有效的方法。
英文摘要：
With advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs), data-driven approach has become a common recipe for various tasks such as knowledge tracing and learning path recommendation. Unfortunately, collecting real students' interaction data is often challenging, which results in the lack of public large-scale benchmark dataset reflecting a wide variety of student behaviors in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS, are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models and limits the recorded behaviors to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with artificial intelligence tutoring system. EdNet contains 131,441,538 interactions from 784,309 students collected over more than 2 years, which is the largest among the ITS datasets released to the public so far. Unlike existing datasets, EdNet provides a wide variety of student actions ranging from question-solving to lecture consumption and item purchasing. Also, EdNet has a hierarchical structure where the student actions are divided into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be extended to different domains easily. The dataset is publicly released under Creative Commons Attribution-NonCommercial 4.0 International license for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state of the art models and encourage the development of practical and effective methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
