Learning engineering applies data and learning science principles to better understand outcomes and support improvement research. One important approach is A/B testing-common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE), and the International Consortium for Innovation and Collaboration in Learning Engineering (IEEE ICICLE). Several systems supporting A/B testing in educational applications have arisen recently, including UpGrade, E-TRIALS, and Terracotta. A/B testing can help improve educational platforms, yet there are challenging issues unique to conducting such work in these contexts. In response, a number of digital learning platforms have opened 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 is conducted in educational contexts, how digital learning platforms are accelerating education research, and how empirical approaches can be used to drive powerful gains in student learning. It will also discuss opportunities for funding to conduct platform-enabled learning engineering.

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), and the International Consortium for Innovation and Collaboration in Learning Engineering (IEEE ICICLE). Recently, several A/B testing systems have arisen that focus on conducting research in educational environments, including UpGrade, Terracotta, and E-TRIALS. A/B testing can help improve educational platforms, yet there are challenging issues unique to conducting such work in these contexts. In response, a number of digital learning platforms have opened 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 challenges of A/B testing in educational contexts, how learning platforms are accelerating education research, and how empirical approaches can be used to drive powerful gains in student learning. It will also discuss 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.

To promote inclusion of students with marginalized identities, districts need to develop comprehensive inclusive computing pathways across grade levels. Working in a Research Practice Partnership (RPP), we have co-designed a district-facing toolkit to support the creation of these pathways. In this poster, we present both the CT Pathways Toolkit and results from four districts piloting it. We examine the experiences of the pilot districts and analyze trends in the toolkit's use. This work expands knowledge about building inclusive computing pathways and computational thinking (CT) integration within K-12 schools.

Three districts representing unique contexts and challenges from across the United States have been iteratively designing and implementing inclusive computing pathways for K-12 students. In this paper, we identify barriers to inclusivity within each districts? K-12 computing pathway. Through a research practice partnership (RPP), we seek to develop a shared understanding of inclusiveness and apply that understanding to the development of targeted supports in the design and implementation of K-12 computing pathways. We analyze existing structures and systems for gaps in order to create new opportunities and resources for students, teachers, schools and districts.

The K-12 CS Framework provides guidance on what concepts and practices students are expected to know and demonstrate within different grade bands. For these guidelines to be useful in CS education, a critical next step is to translate the guidelines to explicit learning targets and design aligned instructional tools and assessments. Our research and development goal in this paper is to design a playful, curriculum-neutral assessment aligned with the 'Data and Analysis' concept (grades 6-8) from the CS framework. Using Evidence Centered Design and Participatory Design, we present a set of assessment guidelines for assessing data and analysis, as well as a set of design considerations for integrating data and analysis across middle school curricula in CS and non-CS contexts. We outline these contributions, describe how they were applied to the development of a game-based formative assessment for data and analysis, and present preliminary findings on student understanding and challenges inferred from student gameplay.

The term “Cyberlearning” is used in the United States to describe a community of researchers, largely funded by the US National Science Foundation, who are exploring the integration of computer science research with learning sciences research. The Cyberlearning community is parallel to the EC-TEL community and the purpose of this poster is to foster mutual engagement between the communities. The paper describes the origin of the term, the conception of the field, the kinds of research being conducted, and some of the exemplary projects. The paper will also introduce the Center for Innovative Research in Cyberlearning (CIRCL), which is the hub of the knowledge network (research community) for cyberlearning and hosts a useful collection of resources.

A series of reports on Innovating Pedagogy were launched in 2012 to look at the trends that show how practitioners may engage in innovation in pedagogy. This paper looks at the latest set of trends, and highlights four 2015 trends that seem particularly rich for researchers to explore in the next five years.

How students do homework has been under-researched relative to classroom learning because it is more difficult to collect data on students' homework behaviors. Presumably, such data would have implications for students' achievement. To understand how students do homework and how homework performance and behaviors relate to end-of-year standardized test scores, we analyzed the system logs from an online homework support platform used by more than 1,500 seventh-grade students in Maine.

No abstract available.

This paper will discuss a framework and methodology for understanding the use of computers in collaborative learning. In particular, we are interested in how learning occurs when students work together using a computer microworld. Collaborative settings provide a particularly rich environment for studying learning. Many theorists (see Brown and Palinscar, in press) have proposed that learning occurs when students have to explain, develop, or justify their ideas to others. In a collaborative setting, students communicate their ideas in order to coordinate their activity towards shared goals. When dilemmas arise in the course of productive work, the combination of communication and activity can lead to learning (Vygotsky 1978, Dewey 1923, Mead 1934).