All of us, but especially today’s college students, expect technology to be transformational and not merely transactional. For example, I once boasted of my extensive CD collection and now on a device, the size of a cigarette pack, I stream all the world’s music. This week, I navigated Tokyo’s bewildering alleys and back streets with Google Maps. Similarly, a small, remote rural college campus can now offer its students a direct link to the Hubble Space Telescope and no one cares about how many volumes sit in the library—unthinkable only twenty years ago. This is technology as transformational. But when it comes to Learning Management Systems (LMS), we are still mostly working with the transactional. That is about to change.

It is unfair (if common) to blame the longstanding LMS providers for the current limitations of their systems, as they were solving what was then the transactional task asked of them by universities: replicate the transaction of the traditional classroom in an LMS. Those transactions were the ones needed by faculty members to conduct their classes, things like taking and returning assignments, posting grades, sending messages, conducting classroom discussion, and sharing course materials. That generation of LMS providers did a great job and built systems around faculty needs and helped drive the enormous growth in online learning.

In an LRM model, the unit of analysis for student performance is not the course, but the individual student.

However, we are now seeing a paradigmatic shift away from the faculty member/teaching focus that has long characterized higher education to a new student/learning focus. Whereas the LMS of the past encapsulated the whole of the instructor’s course and what was needed to conduct it, next generation systems holistically capture the student’s learning experience. We are now seeing the term Learning Relationship Management (LRM) to describe the new approach and while LRM’s will still need to provide traditional LMS functions (students will continue to take classes, after all), those are more likely to be part of an end point solution in a richer LRM architecture, built around individual student data that captures a student’s progress over the term of their education, rather than performance in a single course.

In an LRM model, the unit of analysis for student performance is not the course (how often has she logged on or what grades does he have or are there assignments not yet completed), but the individual student. A well designed LRM system captures the progression of learning—courses completed, but better yet outcomes and competencies mastered—and by integrating and pulling data from other systems, it can provide a 360 degree view of the student’s learning experience. What were formerly silos can be connected because the student and not the course is at the heart of the system. A good LRM will pull in admissions data, interactions with the career center, calls to the IT help desk, information from the Student Information System (SIS) and more. As an institution, I now have a foundation for powerfully transforming student learning.

How so? Using data analytics and regression analysis of past students, I can do predictive modeling for each student. We know, for example, that there is a higher risk profile for a student who registers much closer to the first day of class, pulls down more financial aid, and transfers in with almost no credits. We can know that a junior has still not contacted the career center. We can know that a distant online student has made four calls to the help desk in 36 hours, always a bad sign. We can build triggers that alert advisors or learning coaches to connect with a student. We can adjust curricular pathways in real-time. And the “we” here is not solely faculty members—it includes advisors, learning coaches, curriculum designers, and more.

An illustration might be useful here. With a next generation LRM, the system would know that Sam, a second semester junior, is struggling in his college math class. Our predictive score projects better performance than the data pulled from the LMS point solution reveals. The system knows Sam has a math exam in four days. Because the LRM provides the communication platform for Sam’s online interactions with the various campus systems he accesses (think of it as a portal on steroids), it allows us to use nudge theory to send Sam a message that says: “Did you know that when our students use the Math Support Center their exam grades improve by 14 percent?” It adds the hours and location of the Center, knowing from Sam’s data that he has never accessed the Center and its services. The system also sends an automated reminder to Sam’s lacrosse coach that Sam has an exam coming up. Think of what has happened in this illustration: we used admission and subsequent student data to do predictive analytics, we tracked Sam’s real-time performance in a class, we recognized his next assessment opportunity, we used a little automated behavioral theory to direct his efforts while reminding him of the upcoming test, and we prompted a bit of human interaction from his coach. The traditional LMS was not built for this future—it was simply not asked to solve for the student-centered paradigm.

There is an enormous amount of excitement in the rapid development of and deployment of artificial intelligence, machine learning and assessment, data analytics, and competency-based education (where we measure actual learning as opposed to how long students sat, the credit hour approach). We designed such a system, now spun off as Motivis Learning Systems, and others are at work to move us from the traditional LMS to this new way of doing things. Unizen is focusing heavily on data integration and analytics, D2L has developed LeaP, an adaptive engine to customize student learning paths, and legacy vendors such as Blackboard are building in more analytics capability. We are reaching the long sought after goal of genuinely personalized learning built around students, in which our key human interactions are better targeted, amplified, and effective with the help of technology. Thus, the term “learning relationship management.” It is a dream for which solutions are now emerging and increasingly available and it will take us from the merely transactional to the powerfully transformative.