Augmented reality (AR) combines digitally generated 3D content with real-world objects that users are looking at. The “virtual” computer-generated 3D content is overlaid on a view of the real world through a specialized display. All augmented reality technologies involve some form of display technology that combines real and virtual content – including headset devices, camera-enabled smartphones and tablets, computer-based webcams, and projectors displaying interactive images on a physical surface. These technologies support real-time tracking of hands, 3D objects, and bodies as they push on or touch virtual objects. This enables a more-natural interaction between the learner and the virtual content. AR technologies support learning by allowing learners to interact with 3D representations; they enable embedded assessments; they support groups of learners engaging with shared virtual objects; and they tap into a child’s natural inclination to play and experiment by moving around and touching and manipulating objects.

History learning in schools often focuses on facts such as dates of important events and names of influential politicians or military leaders. This chapter instead considers history knowledge to be of two types: historical concepts and historical narratives. The chapter reviews two types of conceptual knowledge: first-order concepts like peasants and presidents, and second-order concepts like change and continuity, and cause and consequence. The chapter reviews many features of historical narratives, including the representation of national identity, the role of emotion in learning, the presence of mythical figures and motives, and the national or ethnic search for freedom or territory. A deeper understanding of history requires students to “think historically” – to engage in the professional practices of historians such as gathering evidence, developing theories, and translating the past into the present.

Microgenetic methods are used to analyze moment-to-moment processes of learning, reasoning, and problem-solving. Microgenetic methods are useful when studying learning that does not occur in a straight line from lesser to greater understanding, but rather occurs through a learning trajectory that includes iterative and unpredictable paths and sometimes even setbacks or failure. Microgenetic methods are also useful in studying learning that is mediated by tools and artifacts in the learning environment, and what role those artifacts play in the developing learning trajectory. These methods are time-consuming and it’s not practical to conduct studies with large sample sizes or that occur over very long periods of time; rather, the focus is on developing a deep and thorough understanding of a specific learning environment and then to generalize those findings to a broader range of contexts. Microgenetic methods are particularly well-suited to five types of research questions: questions about the variability or stability of strategies; events that precipitate or initiate change; co-occurring events and processes; trajectories or paths of change; and the rate of change.

This chapter begins by describing what is unique about mathematics that has made it a central topic in the learning sciences. This research has historically been interdisciplinary, drawing on psychology, mathematics research and theory, and mathematics educators. It then describes two distinct approaches – the acquisitionist and the participationist. The acquisitionist approach considers learning to be what happens when an individual learner acquires mathematical knowledge. This part of the chapter reviews research on misconceptions and conceptual change that has been based in Piaget’s constructivist theories. The participationist approach views learning as originating in social interactions in diverse settings such as classrooms, homes and playgrounds, museums, and workplaces. This approach views learning as a collective sociocultural phenomenon, and uses methodologies such as interaction analysis and design-based research. This chapter concludes with a discussion of how teachers learn to teach mathematics.

Mobile learning is learning across multiple contexts using smartphones and tablets, digital watches and fitness bands, wearable tags, and other more specialized devices. In educational applications, these devices are often linked together through the Internet or Bluetooth wireless technology, supporting collaboration and interaction. Mobile devices support seamless learning by extending learning beyond the classroom into everyday real-world experience. Mobile devices support the blending of the “formal learning” of the classroom with “informal learning” that takes place at home, in museums, or with peers. Mobile devices support personalized learning, and yet when networked together they can at the same time support collaboration and group learning. Many mobile devices generate large volumes of data from each student’s device, supporting learning analytics applications.

Metacognition is thinking about the contents and processes of one’s own cognition. Research shows that metacognition plays important roles in most cognitive tasks, from everyday behaviors to problem-solving to expert performance. This chapter focuses on metacognition’s centrality in learning and in self-regulated learning. When learning, people monitor what they know and whether it is aligned with their intended learning outcome. A learner’s ability to monitor effectively is known as calibration. Learners then control their next actions based on their monitoring, and finally they self-regulate the process of monitoring and controlling their learning by shaping and adapting cognition or behavior by reaching forward by planning for future tasks. Research shows that people learn better when they have strong metacognitive abilities and when they can self-regulate their learning effectively.

This chapter reviews research that examines the fundamental cognitive and social processes whereby people learn to read and write. The chapter discusses three types of literate knowledge. First, literacy can be general, such as the ability to decode words or engage in drafting and revision. Second, literacy can be task-specific: learning to read a novel and learning to read a recipe require different declarative and procedural knowledge. Third, literacy can be community-specific, in which members of a community approach a given text using different cognitive and interpretive frameworks. Learning how to read and write requires many distinct cognitive components, from decoding letters to composing and interpreting texts. Literacy also requires the ability to integrate these skills within communities of practice, and these findings are aligned with sociocultural perspectives on learning in all subjects.

This chapter builds on growing trends such as the maker movement, programmable children’s toys like LEGO Mindstorms, and gaming consoles with movement sensors like the Nintendo Wii and the Microsoft Kinect. These technologies include fabrication and construction technologies such as 3D printers, laser cutters, and milling machines; embedded computing where small computational devices – such as motion sensors or LED lights – are inserted into physical artifacts; novel materials such as conductive threads or materials with shape memory; optics and tracking with cameras and GPS position sensing. In recent years, computing technologies have increased in power, decreased in cost, and become dramatically smaller. These features enable wearable devices that can be integrated with children’s crafts, tools, and playgrounds. The chapter reviews technologies including virtual reality, body augmentation, programmable toys like LEGO Mindstorms, and manipulatives that are responsive to body movement.

Effective learning requires conceptual change: learning new and correct knowledge while also overcoming and transforming previously held incorrect knowledge. These incorrect conceptions prevent deep learning unless they are transformed into correct ones. These early, common-sense, and incorrect beliefs are sometimes called naïve theories. Most of the research in this chapter concerns physics, biology, and math learning. Jean Piaget’s theoretical work provides an important foundation for conceptual change research, as well as theories in the history and philosophy of science about how scientific disciplines change over time. The author presents a knowledge in pieces theory of how conceptual change occurs during learning.

A complex system is composed of many elements that interact with each other and their environment. The term emergence is used to describe how the large-scale features of the complex system arise from interactions between the components, and these system-level features are called emergent phenomena. This chapter reviews the multidisciplinary study of complex systems in physics, biology, and social sciences. This chapter reviews three topics: first, research on how people learn how to think about complex systems; second, how learning environments themselves can be analyzed as complex systems; and finally, how the analytic methods of complexity science – such as computer modeling – can be applied to the learning sciences. The chapter summarizes challenges and future opportunities for helping students learn about complex systems and for research in the learning sciences that considers educational systems to be complex phenomena.

Learning and teaching are fundamentally cultural processes. Culture is the constellations of practices that communities have historically developed and dynamically shaped in order to accomplish the purposes they value, including the tools they use, the social networks with which they are connected, the ways they organize joint activity, and their ways of conceptualizing and engaging with the world. This chapter reviews research on the cultural nature of learning, including studies of (1) learning in and out of schools; (2) relationships between everyday and academic knowledge and discourse; (3) classroom-based design research that explores linkages between students’ diverse repertoires of practice and those of the academic disciplines being taught. This review addresses multiple dimensions of learning including cognition, discourse, affect, motivation, and identity. The research has implications for several issues in the learning sciences: How does learning interact with community practices? How can we connect these community practices to academic disciplinary practices? How can we use our understanding of community practices to support deeper learning?

This chapter presents an approach to the study of learning which analyzes small groups – such as a dyad, a group, a classroom – or large groups – for example, a community or a social movement. This approach is based on a situativity or sociocultural theory of cognition and learning, where learning is “situated” within complex social and material contexts known as activity systems. Sociocultural theories are related to cultural-historical activity theory, situated learning theory, distributed cognition theory, and cultural psychology. These theories suggest that a full account of learning must extend beyond individualist psychology to analyze and explain social practices, technological artifacts, interactional patterns, and the different roles occupied by the participants.

Understanding individuals’ interest, motivation, and engagement is essential to designing for meaningful learning. We typically think of engaged learners as those who have a more developed interest in content (e.g., math, robotics, swimming) and are motivated to learn. But learners who are not engaged or who are unmotivated can also be assisted to meaningfully engage with content in ways that lead to deep learning. This chapter summarizes research on two questions for how to design for meaningful learning: What supports unmotivated individuals to become motivated to learn? How do we design tasks that enable those who are already engaged to continue to deepen their interest? The chapter summarizes five research studies that provide converging evidence that designing for meaningful learning requires (1) addressing the differences in learners’ interest, motivation, and engagement; (2) supporting learners in engaging in thinking about content with others. Learning environments can be designed to enable all learners, regardless of their initial engagement with material, to develop meaningful connections to content, thus optimizing their learning.

This chapter reviews research on learning in science centers, art museums, children’s museums, zoos, aquariums, botanical gardens, and natural and cultural history museums. These are sometimes referred to as free choice learning environments because visitors are guided by their own interests, not by a predetermined curriculum. Museum learning is public and social, whether in peer groups or with families. Museums expand our definition of learning; they require learning scientists to account for forms of knowledge, behaviors, and interactions that are often different from those in school. This chapter identifies the key features of museums as unique learning environments; it reviews research on family learning in museums; and it reviews how museums are extending their educational mission by working together with schools, community organizations and networks, and citizen science initiatives.