Academic Fitbits

Posted on: July 15th, 2016 by Carol Scheidenhelm

Twitter. Fitbit. Pokemon Go. When many of us began teaching and working in higher education, these concepts simply did not exist (to be fair, one would have to be just weeks old not to pre-date Pokemon Go): we called or wrote people to share our ideas; If we wanted to measure our activity, we had a pedometer or we estimated; finding little imaginary creatures WAS an imaginary exercise. Our students today are exposed to and expect many forms of immediate feedback and reinforcement. But can any of these contemporary technologies providing rapid feedback actually lead to a better understanding of student performance?

In our October 6, 2014 blog entry, we first reported about Quinlan School of Business faculty Linda Tuncay-Zayer and Stacy Neier Beran and their academic use of Twitter.  The post talked about leveraging popular technologies with academic expectations. While it may be a bit longer for concepts behind Pokemon Go to make the ranks of academic technologies (though I strongly suspect someone will find a connection), there are research studies that explore the value of providing student with data of their achievement as motivation for improvement.

The Education Advisory Board (EAB) Daily Briefing recently featured an analysis of how the concept of a Fitbit, which provides regular updates on exercise and certain body metrics, may well be a great example of how motivational data can be. Yes, I strive to make my 10,000 steps every day, often walking around the condo in the evening trying to get the final 200+ steps. How might having this type of feedback motivate students to strive harder in their course work?

The EAB brief lists the following eight ways that Fitbit’s collected data and predictive analytics might be a good model for student feedback.

  1. People change their behavior when they can access their own data.
  2. Data tracking allows for real-time analysis and feedback.
  3. Behavioral data is essential.
  4. Data must be easily accessible for predictive analytics to be useful.
  5. Researchers should have freedom to play in a data “sandbox.”
  6. Data might topple your long-held beliefs.
  7. Certain indicators can provide early warnings.
  8. IT works better when silos are broken down.

Daily Briefing Print Education Advisory Board. 4/25/16. https://www.eab.com/daily-briefing.

The bulletin cites findings from Georgia State University asserting that providing students with their performance data actually changed their behavior. Vice Provost Timothy Renick explained that data have to be in the correct shape to make it relatable for students, just as the Fitbit provides straightforward information about daily performance.

At the University of Kentucky, analysists are working to integrate student data into a platform that they hope to tie to real-time data of in-class activity and feedback.  Institutions are universally finding that early-semester performance data can accurately predict the students’ chances for success. Sharing those data with the student can help motivate and pinpoint where the shortcomings that are hindering his or her success.

We teach and work in an age where assessment, data and analytics are daily topics of conversation in higher education publications. But how are we sharing what we are learning with students? How are we helping them analyze the feedback they receive into meaningful data? What should a Loyola’s academic Fitbit look like?

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