10
Research Agenda
We began this report with a discussion of how all learners grow and learn in culturally defined ways in culturally defined contexts. In Chapter 2, the committee laid out a sociocultural view of learning and noted the application of work from the literature on developmental and cross-cultural psychology to understanding learning and education. This discussion set the stage for the discussions of research from cognitive science, neuropsychology, and other fields on numerous processes and functions of learning, specific influences on learning, and applications of this knowledge for lifelong learning and education. Our review has pointed to strengthening connections among these research domains, and how these connections have supported improved understanding of the dynamic nature of learning. In this chapter, we briefly tie together themes from this body of work. We close with a research agenda we hope will guide researchers and funders of research in pursuing even deeper understanding of human learning.
THE DYNAMIC NATURE OF LEARNING
Learning is a dynamic, ongoing process that is simultaneously biological and cultural. Each individual learner functions within a complex developmental, cognitive, physical, social, and cultural system. Factors that are relevant to learning include influences from the microscopic level to the characteristics of the learner’s neighborhood, community, and the time period in which he lives. Further, even at the most basic individual level, evidence shows that brain development and cognition (and the connectivity between cortical areas) are influenced and organized by cultural, social, emotional, and physiological
experiences that contribute to both age-related and individual variability in learning.
Learning involves the orchestration of interconnected networks. There is no learned skill that uses only one part of the brain, and these various brain systems support each aspect of the human experience: social, cognitive, emotional, and cultural functioning and even health and physiological survival. Thus, attention to both individual factors (such as developmental stage; physical, emotional, and mental health; and interests and motivations) and factors external to the individual (such as the environment in which the learner is situated, social and cultural contexts, and opportunities available to learners) is necessary to develop a complete picture of the nature of learning.
We have highlighted research on specific ways that culture interacts with learning—not as an external influence but as a central aspect of being human. For example, studies have illustrated cultural differences in areas of learning including the effects of different numeric systems on brain organization, conceptual models such as one’s model of time, and expectations about learning. Research suggests that cultural differences may contribute to differences in memory, expectations that guide causal reasoning, and other cognitive processes. Cultural values may influence a learner’s mindset and goals, and it has long been established that cultural stereotypes and values can affect a learner’s self-construal, or definition of herself in reference to others; her confidence and expectations as a learner; her goals; and her performance. Culture is reflected in the procedures people use to complete tasks and solve problems, as well as the social emotional dispositions people bring to such tasks. Positive cultural identification can foster engagement with learning and achievement. Culture is also associated with the type or degree of cognitive changes that are manifest with age, and it partly accounts for the significant variety in the trajectories of individual learners.
We have also described many illustrations of the idea, introduced in Chapter 1, that “to learn” is an active verb naming a dynamic process through which humans continuously adapt, through conscious and unconscious physiological and cognitive responses, to the unique circumstances and experiences they encounter. We have focused on key ideas that can be distilled from a diverse body of work to build on the picture of how people learn as it stood in 2000, when HPL I was published.1 That picture has grown more sophisticated, but there is still much more to learn.
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1 As explained in Chapter 1, HPL I refers to How People Learn: Brain, Mind, Experience, and School: Expanded Edition (National Research Council, 2000).
RESEARCH AGENDA
Much is known about the science and practice of how people learn, but this exploration of the diverse and fast-moving research communities that are contributing to this knowledge base has highlighted frontiers where more work is needed. The committee has identified needed research in two broad areas: understanding and embracing variability in learning and the potential uses and impacts of technology for learning. Advances in these areas will not only expand on what is known about how people learn but also support the work of educators in formal and informal learning settings and in workplace training. We describe specific research goals within each of these two broad categories, and we hope they will be useful guides for researchers and funders of research as they set priorities for future work.
Strategic investments in the work described here will undoubtedly require integration across levels of analysis, methods, and theoretical frameworks. We note that new data sources that could be relevant for understanding learning beyond formal schooling (such as administrative records) could provide new research avenues, and that partnerships across fields can spur innovations in the analysis of information from a variety of sources. We hope this report advances that effort by highlighting both robust findings from current research and opportunities to advance knowledge.
Though the body of research on how people learn is vast, it remains limited in terms of study populations, combinations of contexts, and other important factors. Laboratory science does not adequately reflect the circumstances of learning in the classroom, and the classroom application of lessons from laboratory science is often blunt and insufficiently nuanced. At the most fundamental level, more resources are needed to initiate and then maintain the translation of basic research into translation research for the learning sciences, while also allowing discoveries in situ to be brought back to basic research for exploration. In addition, means of establishing and sustaining truly collaborative and interdisciplinary efforts should be sought.
Several streams of research can address these limitations. One is interdisciplinary research that examines how individual variation and developmental and contextual factors, including social, emotional, environmental, institutional, and experiential factors, influence the lifelong learning process and learning outcomes. These research efforts should include the examination of the cross-level effects of social, emotional, and physiological responses to educational activities and their effects on proximal and distal learning
outcomes. In addition, these efforts should address cross-level effects as they occur over different time scales, in order to coordinate short- and long-term supportive effects on different learning outcomes, and they should elucidate the pathways and temporal trajectories associated with events that facilitate or inhibit learning effort and outcomes.
The committee also notes the need for research focused on the ways distinct cultural communities organize learning; how learners adapt across different cultural systems (such as between home and school); and the learning needs of distinct populations, including those with learning disabilities and aging learners. Specific areas of focus should include the following:
Study populations The generalizability and robustness of findings from research on learning are often limited because of oversampling of certain cultural and socioeconomic communities in study populations. Research efforts that include more-diverse study populations are needed to supplement laboratory-based learning research, with the aim of improving its applicability to real-life classroom settings. Moreover, narrowly identifying study participants by, for example, a single race, culture, or ethnicity, should be avoided. Studies that examine cultural and demographic variables and within-group variation will improve understanding of how cultural and individual variability can be maintained and supported in learning situations.
Interest in learning Additional study is needed of the factors that influence situational interest. These factors include the individual’s prior experiences, the role of different learning structures and extrinsic incentives on sustaining interest, mindset orientation, and learning progress over time. Also needed is additional study of the factors and processes by which individuals allocate effort and time across competing and complementary life and educational goals over time.
Role of identity in learning Research is needed to more precisely explain the ways in which beliefs about one’s cognitive abilities modify learning goals and identities. Research is also needed on how different learning experiences combine to shape learning identity and whether there are particular periods of human development during which learning identity is more or less malleable. Finally, additional research is needed to explain how learners integrate perceived sociocultural norms associated with their present and future identities to arrive at learning goals and how these perceptions influence the use of different learning strategies.
Motivation to learn A more unified understanding of motivation is needed that both distinguishes and integrates the many interrelated and often overlapping factors that have been shown to play a role in motivation and learning. These factors include psychological processes, social interactions, and aspects of culture. Research is needed to explore the boundary conditions of current knowledge: To whom do current understandings about motiva-
tion apply and under what circumstances? Research is also needed in ways to influence motivation and support learning across the life span, in order to evaluate practices and test hypotheses derived from theories of motivation and learning in the context of everyday learning environments (schools, homes, and workplaces). Most research on motivation has focused on psychological processes and dyadic or group interactions between peers or between students and teachers during instruction and in the context of a specific activity or task. More research attention is needed to explore how formal school structures and other influences affect psychological motivational processes and how best to promote engagement, persistence, and goal attainment for learners, including through changes to formal education structures.
Self-regulated learning Self-regulation is best understood in the context of specific learning environments and objectives. Three streams of developmental research are needed: (1) studies that explore the development of self-regulation across time and across domains and disciplines, (2) studies that examine effective instruction in self-regulation in respect to individual development, and (3) studies of environments that lend themselves to the autonomous discovery and development of a broad repertoire of self-regulated strategies. Results from such studies could elucidate whether self-regulation is a skill that is fundamental to academic and life success, whether the development of self-regulation can be sustained over time, and at what developmental time period(s) practitioners might most effectively target self-regulation interventions. Research is needed to better explain the relationship between teaching strategies that promote self-regulation and discipline-specific tools for thinking and reasoning within and across subject areas.
Influence of learning environments Further study is needed of how the culture of the learning environment influences learners’ sense of belonging, adaptability, agency, and learning outcomes. Researchers should identify the types of learning associated with particular learning tasks and environments and should track the predicted consequences for learning, motivation, emotion, and social interaction. Finally, research is needed to explain how methods of instruction prime a positive connection between current learning efforts and desired future outcomes.
Learning across the life span The committee advocates the creation of several large-scale pilot studies to create longitudinal databases that span learning experiences and outcomes from infancy to older adulthood. Similar in kind to health databases maintained in Iceland and Sweden, these databases will be an investment in future discovery and the goal of supporting lifelong learning, mental health, productivity, and informed citizenship. The development of comprehensive databases will require decisions about the granularity and content of the database entries that are relevant to a person’s experiences as a learner, as well as considerations of privacy. Because the median age of the U.S. population is increasing, research is needed on ways
to optimize interventions to maintain cognitive and brain health. An immediate goal of such research should be to identify promising interventions and determine their potential efficacy and generalizability. Researchers examining the determinants of learning and development through the life span should look beyond prior achievement and also examine ability, attitude, motivation, and self-regulatory processes for all learners. With respect to postsecondary educational environments, more work is urgently needed on measures that facilitate or impede student performance, as is research on the use of technology to support tailored instruction.
Learning disabilities Advances in experimental design and neuroimaging methods have the potential to substantially improve the ways learning disabilities are defined and diagnosed. Unfortunately, there has been little integration between the field of neuroscience and studies of interventions for learning disabilities. Thus, better merging of data on the results of treatment outcomes and the understanding of underlying conditions provided by the neurosciences is needed to support progress in identifying and remediating disabilities. Technology is also part of the story. Rapidly developing digital, electronic, and mechanical technologies offer promise for the accommodation of a broad set of learning disabilities, but more research is needed to better understand universal design for learning.
Since the publication of HPL I, the use of digital technology in educational settings has skyrocketed. However, digital learning technologies are not always designed using the science of learning as a guide. Furthermore, the design of learning technologies should be tailored to individual learners who function within multiple sociocultural contexts, and the learner must have access to the appropriate technological tool for the right task, in the right context, at the right time point, in order for the technology to facilitate learning. We suggest several lines of research to help ensure that the benefits of learning technologies are maximized.
Because of the variability in learning contexts, there is a need for methods to determine whether a technology is well suited to the ecological learning niche in which it may be used. Comprehensive, systematic meta-analyses of research on the impacts of different learning technologies (e.g., intelligent tutoring systems, mobile apps for memory practice) and technologies with different features (e.g., dialogues with virtual agent, extensive practice with
feedback) for different kinds of learning and for different users can provide the information needed to improve the coordination of learning technologies with desired learning outcomes.
Another issue related to the diversity in learning contexts is the dearth of experimental research shedding light on the transfer of communication skills and habits between online social and academic media. Longitudinal research is needed on the effects of intensive, sustained engagement with online technologies as well as the effects of self-selected online activities on academic learning. This research should examine how online experiences are changing the ways people understand, experience, and engage the world and how these experiences affect academic performance and literacy (e.g., reading, writing, science, mathematics). In terms of the learning environment, some evidence suggests that technology-based learning in informal settings can enhance achievement in school. However, this evidence is not sufficiently strong to guide practice reliably. This gap can be addressed with additional research and development on designs for technology-based interventions linking in-school and out-of-school learning.
There is also a need for research focused on improving the suite of learning technologies available. For example, there is evidence that in an educational setting, very simple computer-teacher interfaces are surprisingly often ignored or quickly abandoned, which would certainly limit their utility in practice. Research and development to investigate the design of digital dashboards and associated instructor training to promote regular and productive use of data from student learning systems to refine instruction will improve classroom utilization of learning technologies. Research should capitalize on (and catalyze) the increasing availability of data from learning technologies and the appearance of new data techniques (e.g., machine learning). These advances will contribute to the design of technologies that support and adapt to variability in the learners who use them in varied contexts.
Likewise, there is currently only limited evidence to support the effectiveness of mobile educational applications or to reliably characterize the impact conversational agents have on learning. Third-party evaluations of the effectiveness of mobile learning applications and subsequent meta-analyses of such studies, once there is a critical mass of evaluation data, can help to address this problem. For example, research is needed on the relative effectiveness of different types of virtual agents, agents with different degrees of domain-specific knowledge, and agents with and without adaptive features, in order to substantiate the utility of such agents in learning environments.
Finally, with respect to learning technologies in the workplace, the committee finds that additional research is needed on the relative effectiveness of supporting embodied cognition through virtual or augmented reality technology and with sensor technologies that can detect nonverbal behaviors, compared to mental manipulation for learning in academic subject areas.
