A significant milestone in our research and development of Precision Education at National University was reached this fall when we launched a digital learning platform we are calling NUNav. The learning navigator platform is being used by students taking introductory algebra and introductory statistics. In two other classes, one on children’s cognitive development and the other a composition class, the platform is being used as a companion to the regular class.
Research is a hallmark of the Precision Education initiative led by the Precision Institute at National University and, with these initial classes and the data they generate, we’re able to test our hypothesis that we can use 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.
Think of the NUNav platform, which we’re designing with help from a variety of industry partners, as the engine under the hood of our overall Precision Education initiative, driven by software and code that will help us to better track the learning approaches, needs and outcomes of students. We are able to generate that data only because of the hard work of participating professors who have unbundled, or broken down, those classes into micro-competencies. For each competency, they came up with at least four learning objects, or activities, that were curated from primarily online resources to help students learn the material. For example, if students were studying how to solve quadratic equations, the learning navigator might suggest they re-read the chapter in the textbook, watch a video, go through a Power Point narration, or listen to a lecture.
Prior to any instruction, students’ mastery of these competencies is assessed at a very granular level to determine an appropriate starting point for teaching. Students can skip content they already know based on their performance on the assessment, allowing them to accelerate their progress. Right now, the platform is serving up the learning objects at random; after studying each micro-competency, students will take a post-test, so we can see how much progress they made. By gathering and analyzing the student data we’ll be able to see which activities are helpful and which ones are not.
Using this method, we found out, for instance, that translating word problems into equations is difficult for virtually every student in the algebra refresher class. The learning activities presented to the students did not erase their confusion. So, the math professor substituted new learning activities, the course will be offered again, and we’ll compare the results. Our hypothesis is that this individualized and flexible guidance will lead to higher levels of engagement and more student success.
Relationship Between Student Characteristics and Student Outcomes
National University classes last only four weeks, which allows us to gather data from a new cohort every month. As we gather more data, we’ll be able to do more in-depth, multi-factor analysis. We’ll try to determine, for example, whether some learning objects work better for certain types of students. We might find out that some learning objects work particularly well with students who are the first in their families to go to college. Or some learning objects might resonate better with students who enter with a certain number of transfer credits. Conversely, we could find out that some student characteristics are not at all correlated with particular learning objects.
The navigator platform will be aligned with another component of the Precision Education initiative, the digital Student Dashboard. The dashboard serves as the dashboard of a car, providing students with real-time data on their current courses, courses passed, and other important information to help them make the best possible decisions about their education. Equally important is how we can better help students plan for their career paths – leveraging the latest in technology and data resources – at the start of their educational journey. We are working with several external groups to integrate career and workforce data into our platform to help students get a better sense of where the jobs are in their areas of interest, and to offer up relevant professional career options.
Another area we are currently running tests on has to do with nudging applications, to see if we can encourage students take specific actions that will lead to greater academic success. We’re in the process of testing two different approaches to nudging – one using social media content and advertisements, and the other using text messages sent to students’ phones.
We’re still in the R&D phase but it’s exciting to see the system we have envisioned taking shape, bringing us closer to our goal of seeing our students succeed. We’ll continue to share what we’re learning through this process of discovery.
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 at: https://www.nu.edu/precision