MOOCdb：开发支持MOOC数据科学的标准和系统
MOOCdb: Developing Standards and Systems to Support MOOC Data Science
摘要：
我们提出了一个共享数据模型，用于在大规模开放式在线课程（MOOC）中实现数据科学。该模型捕捉学生与在线平台的互动。数据模型与平台无关，基于学生在在线学习平台上采取的一些基本核心行动。学生通常以四种不同的模式与平台互动：观察，提交，协作和反馈。在观察模式下，学生只是浏览在线平台，观看视频，阅读材料，阅读书籍或观看论坛。在提交模式中，学生向平台提交信息。这包括对测验，家庭作业或任何评估模块的提交。在协作模式下，学生可以在论坛上与其他学生或教师互动，协作编辑wiki或在google hangout或其他hangout场所聊天。有了这些活动的基本定义，以及存储与这些活动相关的事件的数据模型，我们就可以创建一个通用术语来将Coursera和edX数据映射到这个共享数据模型中。这种称为MOOCdb的共享数据模型成为许多协作框架的基础，这些框架可以在不需要共享数据的情况下实现数据科学的进步。
英文摘要：
We present a shared data model for enabling data science in Massive Open Online Courses (MOOCs). The model captures students interactions with the online platform. The data model is platform agnostic and is based on some basic core actions that students take on an online learning platform. Students usually interact with the platform in four different modes: Observing, Submitting, Collaborating and giving feedback. In observing mode students are simply browsing the online platform, watching videos, reading material, reading book or watching forums. In submitting mode, students submit information to the platform. This includes submissions towards quizzes, homeworks, or any assessment modules. In collaborating mode students interact with other students or instructors on forums, collaboratively editing wiki or chatting on google hangout or other hangout venues. With this basic definitions of activities, and a data model to store events pertaining to these activities, we then create a common terminology to map Coursera and edX data into this shared data model. This shared data model called MOOCdb becomes the foundation for a number of collaborative frameworks that enable progress in data science without the need to share the data.
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。
