capturing how learners interact with content and the discourse they have around learning materials as well as the social networks they form in the process) is driven by the increased attention to online learning. George Siemens: In terms of learning analytics or educational data-mining, the growing externalization of learning activity (i.e.

What new types of educational data can now be captured and mined? Even a simple analysis of existing institutional data could raise the profile of potential at-risk students or reveal attendance or assignment submission patterns that indicate the need for additional support.

Unfortunately, most schools and universities do very little with this wealth of data, other than possibly producing an annual institutional profile report. Privacy laws differ from country to country, but generally will prohibit academics from accessing data that is not relevant to a particular class, course, or program. Most universities will store and aggregate this data under the umbrella of institutional statistics.

This data includes any combination of: location, previous learning activities, health concerns (physical and emotional/mental), attendance, grades, socio-economic data (parental income), parental status, and so on. George Siemens: Schools and universities have long tracked a broad range of learner data - often drawn from applications (universities) or enrollment forms (schools). What kinds of data have schools traditionally tracked? I asked education theorist George Siemens, from the Technology Enhanced Knowledge Research Institute at Athabasca University, about the possibilities and challenges for data, teaching, and learning. Get a free trial today and find answers on the fly, or master something new and useful. Join the O'Reilly online learning platform.

Tools for data analysis in eduction