MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data
MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data
复制标题
MORF：使用隐私受限的 MOOC 数据进行大规模预测建模和复制的框架
DOI：
10.1109/bigdata.2018.8621874
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Baker
中科院分区：
文献类型：
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作者：
Josh Gardner;Christopher A. Brooks;J. M. Andres;R. Baker
大规模开放在线课程（MOOCs）等在线学习平台的大数据库为大规模推进教育研究和影响全球学习者群体提供了前所未有的机会。迄今为止，此类研究一直受到可重复性差和缺乏复制的阻碍，主要是由于三种类型的障碍：实验、推断和数据。我们提出了一个用于大规模计算研究的新系统，MOOC复制框架（MORF），以共同解决这些障碍。我们将讨论MORF的体系结构，这是一个开源的平台即服务（PaaS），它包括一个简单、灵活的软件API，提供与高性能计算环境集成的多种研究模式（预测建模或生产规则分析）。在MORF上进行的所有实验都使用可执行的Docker容器，这确保了完全的可重复性，同时允许使用任何可以安装在基于linux的Docker容器中的软件或语言。每个实验工件都被分配了DOI，并公开提供。MORF有潜力加速和民主化对其海量数据存储库的研究，该平台上进行的初步研究表明，目前包括200多个mooc。我们还强调了MORF为其他领域的计算研究人员所面临的更一般类型的问题提供解决方案模板的方法。
Big data repositories from online learning platforms such as Massive Open Online Courses (MOOCs) represent an unprecedented opportunity to advance research on education at scale and impact a global population of learners. To date, such research has been hindered by poor reproducibility and a lack of replication, largely due to three types of barriers: experimental, inferential, and data. We present a novel system for large-scale computational research, the MOOC Replication Framework (MORF), to jointly address these barriers. We discuss MORF’s architecture, an open- source platform-as-a-service (PaaS) which includes a simple, flexible software API providing for multiple modes of research (predictive modeling or production rule analysis) integrated with a high-performance computing environment. All experiments conducted on MORF use executable Docker containers which ensure complete reproducibility while allowing for the use of any software or language which can be installed in the linux-based Docker container. Each experimental artifact is assigned a DOI and made publicly available. MORF has the potential to accelerate and democratize research on its massive data repository, which currently includes over 200 MOOCs, as demonstrated by initial research conducted on the platform. We also highlight ways in which MORF represents a solution template to a more general class of problems faced by computational researchers in other domains.