Analyzing Millions of Submissions to Help MOOC Instructors Understand Problem Solving
暂无分享,去创建一个
We present an analytics framework that helps analyze student behavior during problem solving in Massive open online courses. There are some important differences in how students solve problems online when compared to on campus education. However, massive amounts of information, at a very high granularity, capturing how students access the content and solve problems is currently stored. We present a structured way for assembling, aggregating, visualizing and statistical approaches to analyze this data. We are developing a comprehensive set of tools that will help and inform instructors. This paper presents our first few steps towards achieving that goal.
[1] G. Glass,et al. Statistical methods in education and psychology , 1970 .
[2] A. Tamhane,et al. Multiple Comparison Procedures , 2009 .
[3] Chris Piech,et al. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.
[4] Zachary A. Pardos,et al. MOOCdb: Developing Data Standards for MOOC Data Science , 2013 .