The EDM Lab develops software to assist with their research analyses and will share all of these tools with the research community. We are currently working on the following tools to be released. Once a tool is released this web page will be updated with links to the actual software.

  • The Course Grade Analysis and Networks (CGAN) tool is the EDM Lab’s most ambitious tool. This python-based tool provides a wide variety of analyses of course-grade data (where each record represents one student in a course section with a grade). We are currently working on documenting the tool and generating tutorials for the tool. Once these efforts are complete we will release the tool for public use.
  •  Our Grade Analysis tool takes low level grade information (at the student/instructor course section level) and analyzes the data at the student, instructor,  and department level. For example, it will compute the average grades assigned by different departments and help identify instructors that assign grades that differ from their peers. More information including instructions for accessing the tool are available on the Grade Analysis Tool page.
  • Our Course Sequence Analysis tool runs the Generalized Sequence Pattern (GSP) algorithm on student course data to identify course sequences that are commonly taken by students. The GSP algorithm is an extension of the Apriori algorithm that finds frequent itemsets (the extension is to handle sequential data). This tool will soon be available for public use and at that point we will update this webpage