ABSTRACT
In this chapter, the authors step back from the details of data analysis and collaborative data-intensive improvement (CDI) practices and tools to reflect on the challenges that this kind of work poses for the participants who need to make it work. They consider the changes in practice that CDI requires on the parts of education researchers, data scientists, education leaders, and frontline practitioners, as well as learning technology vendors and developers. The authors then reflect on some of the lessons they have learned from their own and others' early CDI efforts and offer some predictions with regard to future trends. They produce an invitation to others to undertake this kind of work and contribute their own insights into how to do it most productively. CDI uses data for these three different purposes—understanding, prediction, and assessing changes—and it's important that researchers let the team's current purpose drive the analyses they run.
