Learning engineering adds tools and processes to learning platforms to support improvement research. One kind of tool is A/B testing-common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE). A number of A/B testing systems focused on educational apps have arisen recently, including UpGrade and E-TRIALS. A/B testing can help improve educational platforms, yet challenging issues in education go beyond the generic paradigm. In response, a number of of digital learning platforms is opening their systems to learning-improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing in educational contexts is different, how learning platforms are opening up new possibilities, and how these empirical approaches can be used to drive powerful gains in student learning. It will also discuss forthcoming opportunities for funding to conduct platform-enabled learning research.

Learning engineering adds tools and processes to learning platforms to support improvement research. One kind of tool is A/B testing, which is common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE). A number of A/B testing systems focused on educational applications have arisen recently, including UpGrade and E-TRIALS. A/B testing can be part of the puzzle of how to improve educational platforms, and yet challenging issues in education go beyond the generic paradigm. For example, the importance of teachers and instructors to learning means that students are not only connecting with software as individuals, but also as part of a shared classroom experience. Further, learning in topics like mathematics can be highly dependent on prior learning, and thus A or B may not be better overall, but only in interaction with prior knowledge. In response, a set of learning platforms is opening their systems to improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing in educational contexts is different, how learning platforms are opening up new possibilities, and how these empirical approaches can be used to drive powerful gains in student learning. It will also discuss forthcoming opportunities for funding to conduct platform-enabled learning research.

The emerging discipline of Learning Engineering is focused on putting into place tools and processes that use the science of learning as a basis for improving educational outcomes. An important part of Learning Engineering focuses on improving the effectiveness of educational software. In many software domains, A/B testing has become a prominent technique to achieve the software's goals. Many large companies (Amazon, Google, Facebook, etc.) run thousands of AB tests and present at the Annual Conference on Digital Experimentation (CODE), but that venue is too broad to address AB testing issues specific to EdTech platforms. We see a need to address issues with running large-scale A/B tests within the educational context, where the use of A/B testing lags other industries. This workshop will explore ways in which A/B testing in educational contexts differs from other domains and proposals to overcome current challenges so that this approach can become a more useful tool in the learning engineer's toolbox.

Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online experiments can serve as a third setting for experimental data collection. In this paper, we introduce the Super Experiment Framework (SEF), which describes how internet-scale experiments can inform and be informed by classroom and lab experiments. We apply the framework to a research project implementing learning games for mathematics that is collecting hundreds of thousands of data trials weekly. We show that the framework allows findings from the lab-scale, classroom-scale and internet-scale experiments to inform each other in a rapid complementary feedback loop.