Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered,…

In this work, we describe a new statistical method to improve the detection of treatment effects in interventions. We call our method TAME (Trained Across Multiple Experiments). TAME takes advantage of multiple experiments with similar designs to create a single model. We use this model to predict the outcome of the dependent variable in unseen…

Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a…

Background/Context: Large-scale randomized controlled experiments conducted in authentic learning environments are commonly high stakes, carrying extensive costs and requiring lengthy commitments for all-or-nothing results amidst many potential obstacles. Educational technologies harbor an untapped potential to provide researchers with access to…

Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system…

Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students--explanations, feedback, and other pedagogical interactions. Considering the…

Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…