How are Big Data & Analytics Being Used in Education?

One of the questions I've had regarding Learning Analytics and Educational Data Mining is, how are they being used in education?  Or to put it another way, what is the "job" that is trying to get done by appling LA & EDM?  This question seeks to understand the value that these methodologies, processes, and networks add to education.  This isn't answering the how question, but instead tries to understand what problems, or opportunities, or applications form the context of LA & EDM.
 Siemens and Long (2011) provide a wide breadth of value-adding activity.  I have transformed the narrative description into a numerical list simply to discuss further detail about select activities. 

1. Improve administrative decision-making and resource allocation
2. Flag at-risk students and provide intervention
3. Create shared understanding of the institution's successes and challenges
4. Transform college/university system
5. Transform academic models and pedagogical approaches
6. Make sense of complex topics
7. Conduct what-if scenarios and experiments to explore elements in a complex discipline (i.e., retaining students, reducing costs)
8. Provide up-to-date information to handle immediate challenges
9. Determine value generated by faculty including hard (research,patents) and soft (reputation, profile, teaching quality)
10. Inform learners about their own learning habits, give recommendations for improvement

Let me regroup this list into 3 buckets:

A) University Administrators (#1, #3, #4, #7, #9)
B) Faculty (#2, #5, #9)
C) Learners (#2, #6, #10)

 The A bucket seems to line up well with what is called Academic Analytics.  This is discussed more in Curtis' post here.

Bucket B deals with #2 (flagging at-risk students), #5 (transforming academic models and pedagogical approaches), and #9 (determining value generated by faculty).  The example used in the article regarding flagging at-risk students uses data such as hours viewing content and the number of pages viewed as the kind of data used. 

Bucket C identifies opportunities for LA & EDM that benefit the learner including #2 (identifying at-risk students and providing intervention), #6 (making sense of complex topics), and #10 (informing learners about their learning habits). 

Thus, LA & EDM seek to support the full range of participating constituents in higher education.