In this often-cited article, Phil Long and George Siemens situate the field of learning analytics between the need for higher education reform and our increased access to student data. They distinguish learning analytics from academic analytics on the basis of their different objects of analysis: where academic analytics is primarily concerned with institutional performance relative to peers, learning analytics centers on the contexts in which learning takes place (i.e. the classroom and the department). For Long and Siemens, learning management system (LMS) data is a great place to start, but they look forward to innovations that would allow for the collection and analysis of more varied and distributed data sources including physical world data, clickers, and social media. They argue that extracted analytics are not enough, but rather that the future lies in our ability to make use of data and algorithms to produce responsive curriculums and intelligent tutoring systems.