Surveying the MOOC Data Set Universe

James J. Lohse, Christine A. McManus, David A. Joyner
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引用次数: 2

Abstract

This paper is a survey of the availability of open data sets generated from Massively Open Online Courses (MOOCs). This log data allows researchers to analyze and predict student performance. Often, the goal of the analysis is to focus on at-risk students who are not likely to finish a course. There is a growing gap between the average researcher (who does not have access to proprietary data) and the ready availability of data sets for analysis. Most research papers studying and predicting student performance in MOOCs are done on proprietary data sets that are not anonymized (de-identified) or released for general study. There are no standardized tools that provide a gateway to access usable data sets; instead, the researcher must navigate a maze of sites with different data structures and varying data access policies. To our knowledge, no open data sets are being produced, and have not been since 2016. The authors survey the history of MOOC data sharing, identify the few available open data sets, and discuss a path forward to increase the reproducibility of MOOC research.
调查MOOC数据集宇宙
本文是对大规模开放在线课程(MOOCs)生成的开放数据集的可用性的调查。这些日志数据使研究人员能够分析和预测学生的表现。通常,分析的目标是关注那些不太可能完成课程的高危学生。普通研究人员(无法访问专有数据)与可供分析的数据集之间的差距越来越大。大多数研究和预测mooc学生表现的研究论文都是在专有数据集上完成的,这些数据集没有匿名(去识别),也没有公开供一般研究使用。没有标准化的工具提供访问可用数据集的网关;相反,研究人员必须浏览具有不同数据结构和不同数据访问策略的网站。据我们所知,目前还没有公开的数据集,而且自2016年以来就没有。作者调查了MOOC数据共享的历史,确定了少数可用的开放数据集,并讨论了提高MOOC研究可重复性的路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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