Halverson is the School of Education’s associate dean for innovation, outreach and partnerships. He also is professor with the Department of Educational Leadership and Policy Analysis, and the director of the Wisconsin Collaborative Education Research Network (The Network).

This article examines recent trends in using tracking data in schools, focusing on the Madison Metropolitan School District (MMSD). The article notes that the ninth-grade-on-track benchmark is predictive of future success, and students who are “on track” are more likely to graduate on time. To be considered on track, students should have a 90 percent attendance rate, a 3.0 grade-point average, and no more than two failing grades.

Madison East High School uses this data to determine whether students are high opportunity, opportunity, vulnerable, or high risk. Teachers are learning how to implement this data into the way they teach.

Halverson tells the Capital Times that MMSD data tracking efforts are founded in predictive analytics, saying “predictive analytics is where you try to use records of student performance to predict where they’re going to be so you can reach out to students and intervene.”

According to Halverson, some school districts in Wisconsin are taking a personalized learning approach in response to this data. A personalized learning environment allows teachers to work with students on a more individual basis, gauging their interests, personalizing the order of lessons, and understanding how the student will display mastery of material. Halverson says that personal learning is “much more about instruction than diagnosis, and it changes the way that teaching happens . . . It’s quite controversial in some ways because if it’s not done right, it’s not effective.”

With the movement to ingrate tracking data into instruction, MMSD and districts across Wisconsin are now struggling with measuring qualitative aspects of a student’s life, like social and emotional learning. Halverson notes that the practices of measuring such things are not yet well articulated in research.

The article also notes the importance of remembering the person behind the data point. Halverson suggests that teachers’ classroom experience should be integrated with the data system signals, to offer a more complete understanding of a student’s behavior and performance.

Read the complete Capital Times article here.