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.

Visual comparisons are pervasive in science, technology, engineering, and mathematics (STEM) instruction and practice. In previous work, adults' visual comparisons of simple stimuli were faster and more accurate when the layout of a display facilitated alignment of corresponding elements-the spatial alignment principle (Matlen et al., 2020). Here, we asked whether the spatial alignment principle extends to rich, educationally relevant stimuli, and how prior experience and spatial skill relate to spatial alignment effects. Participants were asked to find an incorrect bone within a skeleton, presented individually or paired with a correct skeleton in a layout that did (direct placement) or did not (impeded placement) support alignment (Kurtz & Gentner, 2013). Consistent with the spatial alignment principle, undergraduates (Study 1) showed an advantage of direct over impeded placement. Middle schoolers (Study 2) showed a direct advantage on items presented in atypical orientations. That atypical items showed the strongest effects suggests that direct placement may help most when materials are less familiar. However, neither individual differences in undergraduates' STEM course history, nor undergraduates' or middle schoolers' spatial skills moderated spatial alignment effects. Thus, applying the spatial alignment principle in science, technology, engineering, and mathematics has potential to improve visual comparisons, especially those that are challenging, for students of all spatial skill levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Images, such as photographs and diagrams, play an important role in the teaching and learning of science. To optimize student learning, educational science images should be designed to facilitate the cognitive processes relevant to comprehension. One such process is comparison, which involves aligning multiple representations on the basis of their common relational structure. This structural alignment process can be facilitated by cognitive supports that are inherent to an image, including its spatial layout. Yet, little is known about the extent to which students must engage in comparison to learn from science images, and whether widely-used educational materials are conducive to structural alignment. To address these issues, we sampled multiple chapters from each of three popular U.S. middle school life science textbooks. We coded each image for the presence of prompts for comparison using cues within the images and surrounding text. For each image that prompted comparison, we coded whether its layout facilitated relevant structural alignment ( direct placement of matched pairs) or obscured alignment ( impeded placement). Overall, we found that comparisons were prompted for more than a third of the images. However, fewer than half of the images that required comparison had a spatial layout that provided strong support for comparison—that is, direct placement of matched objects/parts. We propose that, in concert with other cognitive supports for learning from multiple representations, spatial supports for comparison could be applied broadly to increase the effectiveness of educational science images.

Humans have a uniquely sophisticated ability to see past superficial features and to understand the relational structure of the world around us. This ability often requires that we compare structures, finding commonalities and differences across visual depictions that are arranged in space, such as maps, graphs, or diagrams. Although such visual comparison of relational structures is ubiquitous in classrooms, textbooks, and news media, surprisingly little is known about how to facilitate this process. Here we suggest a new principle of spatial alignment, whereby visual comparison is substantially more efficient when visuals are placed perpendicular to their structural axes, such that the matching components of the visuals are in direct alignment. In four experiments, this direct alignment led to faster and more accurate comparison than other placements of the same patterns. We discuss the spatial alignment principle in connection to broader work on relational comparison and describe its implications for design and instruction. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Author(s): Matlen, Bryan; Gentner, Dedre; Simms, Nina; Zheng, Yinyuan; Jee, Benjamin | Abstract: Visual comparison is used in education to convey important commonalties and differences. This process is more effective when the figures are spatially aligned so that the corresponding parts and relations are maximally clear (direct placement) (Matlen, Gentner, a Franconeri, 2020). Yet science textbooks often fail to follow this principle arranging figures meant to be compared (Jee et al., in prep)—perhaps in service of visual appeal. To explore whether this choice in fact maximizes visual appeal, we gave middle-school students illustrations characteristic of textbook figures, along with modified versions that followed direct placement principles. Students were significantly more likely to choose the direct placement version when given the goal of helping other students see differences among the figures (M=94%), than when given the goal to make the figure “look nice” (M=61%). These findings suggest that direct placements improve the educational value of a figure without sacrificing its aesthetic appeal.

How can research findings from cognitive and learning sciences be meaningfully applied in authentic settings to improve student learning outcomes in mathematics? Decades of basic research on how people learn has implications for the design of curriculum, instruction, and assessment. However, bringing research to practice involves simultaneously applying multiple design principles and raises pragmatic challenges of classroom contexts. Our project used research-based recommendations to systematically revise a widely used middle school mathematics curriculum and investigated whether the revised curriculum improved student learning in mathematics. In this article, we detail a replicable process for operationalizing and implementing multiple research-based principles and report findings from a large-scale experimental evaluation of this approach to estimate the potential impact on student learning.

The production of a scientifically literate population is a fundamental goal of our educational system. The justifications for that goal, and descriptions of paths toward it, have been reiterated many times in recent decades, as exemplified by major policy statements and specific recommendations from prestigious organizations ranging from "Benchmarks of Scientific Literacy" (AAAS, 1993) to the recent "Framework for K-12 Science Education" (NRC, 2012). Consequently, substantial effort has been devoted to determining how to increase the likelihood that, as students progress through school, they will acquire at least a rudimentary understanding of fundamental domain-general scientific concepts and procedures, as well as a nontrivial amount of domain-specific concepts. However, given the vast number of those procedures and concepts, it is not surprising that the full science curriculum presented to students from pre-school through high school has often been characterized as "a mile wide and an inch deep" (Li, Klahr, & Siler, 2006; Santau et al., 2014). Thus, the challenge facing researchers interested in improving science education is to enhance the quality and generality of the answers to two related questions: What is scientific thinking? and How can it be taught? In this chapter, we attempt to answer the first question by presenting a brief summary of a broad framework that characterizes the essential aspects of scientific thinking and reviewing the developmental origins of scientific thinking. We answer the second question by describing a few representative examples of research on teaching science in specific domains, such as physics, biology, and earth sciences - organized according to the framework - and selected from the extensive literature on different ways to improve children's basic ability to think scientifically.

Professional development holds significant potential in promoting science education, but that potential is undermined if instructional changes are not sustained. For professional development, sustainability refers to the continuation of outcomes over an extended period of time after the program ends and is an issue across international contexts. This longitudinal research, part of a larger research project funded by NSF, investigated sustainability of early elementary science instruction after teacher participation in a 3-year professional development program and the factors that influenced teachers’ decisions about instructional time and strategies. The research used a case-study approach to obtain an in-depth understanding of why instructional shifts occurred over a period of 7-years. The primary data sources were teacher surveys, self-efficacy assessments, and interviews. The findings highlight how school level factors changed over time and affected teachers’ science instruction in both positive and negative ways. The research illustrates the connection between contextual constraints and science instruction and holds implications for sustaining meaningful instructional changes after professional development ends.

Mathematical problem solving typically involves manipulating visual symbols (e.g., equations), and prior research suggests that those symbols serve as diagrammatic representations (e.g., Landy and Goldstone 2010). The present work examines the ways that instructional design of student engagement with these diagrammatic representations may impact student learning. We report on two studies. The first describes systematic cross-cultural differences in the ways that teachers use mathematical representations as diagrammatic supports during middle school mathematics lessons, finding that teachers in two higher achieving regions, Hong Kong, and Japan, more frequently provided multiple layers of support for engaging with these diagrams (e.g. making them visible for a longer period, using linking gestures, and drawing on familiarity in those representations), than teachers in the U.S., a lower achieving region. In Study 2, we experimentally manipulated the amount of diagrammatic support for visually presented problems in a video-based fifth-grade lesson on proportional reasoning to determine whether these multiple layers of support impact learning. Results suggest that learning was optimized when supports were used in combination. Taken together, these studies suggest that providing visual, temporal, and familiarity cues as supports for learning from a diagrammatic representation is likely to improve mathematics learning, but that administering these supports non-systematically is likely to be overall less effective.

Educational technology development is a design problem. Product developers must optimize between what educational research suggests would be most effective, technological or other software development constraints, and the practical needs of end users and key stakeholders. Creating a logic model and using it to guide a user research program can help product developers tackle this problem. A logic model is a structured description of how a specific product achieves an intended learning outcome. Developing a logic model helps product developers make explicit their assumptions about users, product features, and use cases. Then a user research program can be constructed to test each of these assumptions and provide actionable feedback for further iterations of the product. In this chapter, we present three cases that highlight how the logic model approach can guide a program of research, and how that research has led to tangible product improvements.

Individual differences in executive functions (EFs) are well established to be related to mathematics achievement, yet the mechanisms by which this occurs are not well understood. Comparing representations (problems, solutions, concepts) is central to mathematical thinking, and relational reasoning is known to rely upon EF resources. The current manuscript explored whether individual differences in EF predicted learning from a conceptually demanding mathematics lesson requiring relational reasoning. Analyses revealed that variations in EF predicted learning when measured at a delay. Thus, EF capacity may impact students’ overall mathematics achievement by constraining their resources available to learn from cognitively demanding reasoning opportunities in lessons. To assess the ecological validity of this interpretation, we report follow-up interviews with mathematics teachers who raised similar concerns that cognitively demanding activities such as comparing multiple representations in mathematics may differentially benefit their high versus struggling learners. Broader implications for ensuring that all students have access to, and benefit from, conceptually rich mathematics lessons are discussed. We also highlight the utility of integrating methods in science of learning (SL) research.