We at DA keep our ears to the ground and our noses to the grindstone always looking for new stuff to keep you, our readers, well informed. Much of what we’re hearing these days points toward the growing use of predictive analysis—looking at student data and seeing where kids are going, rather than looking at where they’ve been, as is used with data-driven decision making. Sophisticated modeling software is beginning to move from the corporate world and higher education admissions to K12, and the potential is huge.
The larger promise of predictive analysis is that when correctly applied and interpreted, it will enable educators to identify more clearly what students need and to customize instruction appropriately. This has implications not simply for individual student performance, but in how educators perceive teaching, learning and assessment. By offering information in real time, predictive analysis can support quick change and raise student high school graduation and college completion rates. But there is one danger, and that is simply reducing learning to a set of numbers and optimizing across the numbers. Managing Editor Angela Pascopella explores this topic in “A Crystal Ball for Student Learning.”
Also this issue, we include the first in a series of profiles of education reformers. “Models for Education Reform” begins with a focus on Robert J. Marzano, the co-founder and CEO of Marzano Research Laboratory in Englewood, Colorado. His model is built on three critical commitments that school administrators must make to their students.
Your feedback is welcomed @firstname.lastname@example.org