What Higher Education Can Learn from Fitbit
Today, technology is allowing us to capture unprecedented data on our daily activities. Wearables like Fitbit track movement, calories burned—even sleep. GPS enabled mobile devices capture my car’s every turn. The Internet of Things helps us understand temperature preferences to optimize energy consumption at home. In each case, widespread broadband access coupled with the explosion of mobile computing is making it easy to measure activities that were, historically, almost impossible to capture.
As it turns out, when people realize how infrequently they move, or how little they sleep, they begin to walk up the stairs—or get to bed just a little earlier. Data leads to awareness. And awareness leads users to adapt their behavior in positive ways. We are only beginning to understand the impact of such data on the design of systems when we view the data in aggregate.
In education, entrepreneurs often seem to take the opposite tack. Rather than start with the existing movements and practices of educators and students, they design tools to disrupt the practice of teaching or learning experiences. When institutions and professors shift their approach, they argue, the impact of technology will be transformative. It’s an attitude with an implicit assumption: If only faculty would step aside, technology could save education.
Recently, the use of predictive analytics has garnered well-deserved interest and attention in higher education. Colleges and universities are using academic data and profiles (demographics, financial information, and past performance) to approximate risk and identify cohorts of students in need of additional support. In higher education, we know a lot about what students did but very little about what they are doing. We have models to predict outcomes but haven’t yet approached the equivalent of the clickstream data that transformed online advertising. Early analytics efforts are producing meaningful results, but the real transformation will not happen until real-time data on actual student behavior is introduced into the equation.
