Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test interventions, when informed by data-driven modeling, are often described as "close the loop" studies. Studies that close the loop attempt to link improvements in statistical and machine learning models of learning to real-world learning outcomes. After first considering challenges to internet scale experiments, we review several educational data science/mining studies that close the loop between data-driven modeling and learning outcomes. Next, we describe UpGrade, an open source architecture that, when integrated with educational technologies, helps overcome challenges to large-scale field trials (or internet scale experiments) that close the loop between data-driven work and real-world learning outcomes. In addition to describing preliminary randomized experiments that have been conducted and will soon be conducted using the architecture in two educational technology platforms, we end with a "call for contributors and integrators." UpGrade contributors and integrators will be researchers and developers who seek to drive continuous, data-driven improvements in real-world settings where learning with technology occurs. [For the full proceedings, see ED623995.]