The science of learning has generated a wealth of research and theory on how people think and learn. One product of this research is what are known as 'principles of learning' - teaching and learning strategies that have garnered empirical support for their effectiveness at enhancing learning across a range of learners and domains in rigorous lab and classroom tests. For example, the principle of interleaved practice involves mixing problems that can be solved using different solutions -- to support discrimination learning - and spacing problems that can be solved using the same solution over time -- to support long-term memory retention [1]. In the present work, we describe an on-going cluster randomized trial where we attempt to test the efficacy of interleaved practice across multiple schools and geographic regions in a nationally representative U.S. sample. Despite strong evidence that interleaved practice leads to greater math learning than blocking problems [2], blocked practice predominates math curricula [3]. This finding follows a general trend that learning research is underutilized in educational contexts [4]. One reason for this disconnect is that learning takes place within a dynamic system that is highly contextual [5]. What works in one context may not work the same way in another. As a result, scaling learning research remains a major challenge. Although interleaving is seemingly straightforward for application, we describe several challenges we faced in our own work when attempting to scale interleaving in this large-scale study. To address these challenges we outline a process guided by a working implementation model for translating interleaving to concrete materials. We also describe early results of our first (of three) data collection years assessing the impact of a mostly interleaving intervention relative to a mostly blocked intervention on students' math achievement. Although our work is ongoing, the lessons learned even at this early stage can have important implications for scaling interleaved practice as well as learning principles more broadly.

There has been significant progress in increasing the access to computing education for many K-12 students, including states adopting computer science (CS) standards and/or requiring CS courses. This includes the creation of block-based programming languages to make programming more accessible to younger students. Despite this progress, a new challenge has emerged: Students often struggle to transfer conceptual knowledge when transitioning to a new programming language (e.g., transitioning to a text-based programming after learning a block-based programming language). This poster presents the results of teacher interviews regarding the examples of knowledge transfer they observe in their classrooms. These interviews are part of an overarching project that aims to address the challenge of knowledge transfer between programming languages by developing a framework to support such transfer and deliver curricular supports that can be used to aid students' productive knowledge transfer between programming languages.

The 1980s and 1990s saw a robust connection between computer science education and cognitive psychology as researchers worked to understand how students learn to program. More recently, academic disciplines such as science and engineering have begun drawing on cognitive psychology research and theories of learning to create instructional materials and teacher professional development materials based on theories of learning, to some success. In this paper, we follow a similar approach by highlighting common areas of interest between computer science education and cognitive psychology–specifically theories of analogical transfer–and discuss how cross-pollination of theoretical constructs between disciplines can support research on the teaching and learning of multiple programming languages. We will also discuss areas where computing education research can adapt the existing theories from cognitive psychology to develop domain-specific theories of knowledge transfer in computing and feed back into cognitive psychology research to inform larger debates about the nature of cognition and learning.