The process of what we call “unbundling” the college experience has already revolutionized other aspects of our lives, including how we listen to music. We can choose to listen to individual songs from Spotify, Pandora, or similar services, for example, without having to purchase an entire album. Meanwhile, the system identifies and offers us options for songs that align with our musical preferences and orientation to create an optimal listening experience. In digital format, we can listen to some songs over and over again, or skip others that are not meeting our needs.

Through the system we are developing at National University, which we call Precision Education, we are in the process of unbundling courses so that we can better track and assess the building blocks of knowledge that are essential to course mastery. This is key to achieving our larger goal to move away from a system where students adapt to how we teach to one that gathers as much information as possible about each student and then adapts to how each one learns best, taking into account their current state of knowledge, their academic or career goals, and their particular circumstances.

Mass Customization of Education

As a professor, I appreciate how unbundling in higher education can ideally lead to a more personalized learning environment for students that allows faculty to be even more effective in supporting a range of approaches to learning. By unbundling courses, we can evaluate learning at its most structural level so that we can provide students with the exact right resource at exactly the right time. This process is currently underway at National University where participating professors are looking at their classes and breaking them down into micro-competencies, or discrete skills and concepts.

For each micro-competency, the professor identifies three to five learning objects, such as videos, texts, group projects, simulations, and lectures. The professor also creates a pre-assessment that students take before starting that part of the class and one or more post-assessments to determine how much they’ve learned.

The pre-assessment shows whether a student can skip that unit and move on to the next. Those who don’t score high enough work through the learning objects the system determines are likely to work best for them. If they don’t pass the post-assessment, the system will recommend alternative ways to learn the material. Everyone gets second and third chances, if needed, each time approaching the micro-competency from a slightly different angle or with more help from the professor.

Using Data to Learn What Works Best for Students

By early 2018, professors will have redesigned 20 general education courses. When those classes are up and running, the technology-based platform will gather data continuously, determining which learning objects work best for what type of students, constantly improving to serve students. Facebook does something similar. At any one time, Facebook is running thousands of different versions, collecting data on which ones are most engaging and effective for particular users. The data is fed back into the system, allowing for continuous modification and increasingly precise recommendations.

We’re doing the same thing with the non-instructional support we provide for students. We’ll use data on students’ interactions with the system to reach out to them and offer help earlier, making certain to maximize success. Professors and advisers will know about students’ career goals and emerging obstacles to achieving those goals. To learn more about how one of our faculty members is unbundling a course, please visit this guest post by Nima Salimi.

Blog post written by Dr. David Andrews, President of National University. Precision Education at National University is a research-based initiative that is exploring new ways to leverage technology, open education resources, and predictive data analytics to adapt to student needs and guide them to successful completion of their academic and career goals. Learn more about the initiative and the Precision Institute at National University: