As noted previously, current course management systems do little beyond reporting. Considering how models could be developed using a wide range of academic tools – course management, clickers, podcast, etc. How this data is combined, utilized, and presented offers a number of technical, but more importantly, policy challenges.

Our analytics efforts at Purdue have focused on predicting student success based on the data we have on a student’s aptitude (standardized test scores, high school information, etc.) and the student’s effort within the course (course management system). While there is significant work to be completed, the initial results are promising.

Peter DeBlois, Diana Oblinger and I published an article in EDUCAUSE Review this summer on analytics. You can find it at: http://www.educause.edu/apps/er/erm07/erm0742.asp

Which brings me to the second problem. When you say that attendance should not be private, what does that mean, exactly? We know that LMSs make this information available to faculty today, but what about advisors? Should they be able to see this information? And what if a graduate program finds a correlation between undergraduate student LMS logins and their likelihood of doing well in graduate school? Should they have access to that data too?

Finally–and most importantly, the login metric was just an example. Once we have some real data mining capability in place, there’s no telling what other information might be useful. For example, suppose a correlation is discovered between discussion posting patterns and…I don’t know…post-graduation job placement. Who should have access to that data about a student?

The whole point about data mining in this context is that it’s somewhat speculative. You don’t know what information is going to turn out to be useful to whom and for what purposes. And since you don’t know in advance, you can’t have all the privacy policies worked out in advance either. That’s why you need a flexible architecture of privacy as a foundation before you start.