This chapter returns to the discussion begun in Chapter 2 about the nature of deeper learning and 21st century skills. It opens with an introduction that includes a brief discussion of the goals of deeper learning and a brief discussion of the history of theory and research on transfer. The second and longest section of the chapter discusses cognitive perspectives on deeper learning, reviewing work in cognitive and educational psychology in support of our argument that deeper learning is the process of developing durable, transferable knowledge that can be applied to new situations. In the third section, we offer an example of a learning environment that promotes the processes of deeper learning and develops cognitive, intrapersonal, and interpersonal competencies. In the fourth and fifth sections, we discuss the intrapersonal and interpersonal domains, considering how 21st century competencies in these two domains support the process of deeper learning. The sixth section briefly discusses the implications of the research reviewed throughout the chapter for teaching of deeper learning and 21st century competencies, and the chapter ends with conclusions.
A CLASSIC CONCERN: LEARNING FOR TRANSFER
The committee views the broad call for deeper learning and 21st century skills as reflecting a long-standing issue in education and training—the desire that individuals develop transferable knowledge and skills. Associated with this is the challenge of creating learning environments that support development of the cognitive, intrapersonal, and interpersonal
competencies that enable learners to transfer what they have learned to new situations and new problems. These competencies include both knowledge in a domain and knowledge of how, why, and when to apply this knowledge to answer questions and solve problems—integrated forms of knowledge that we refer to as 21st century competencies and discuss further below.
If the goal of instruction is to prepare students to accomplish tasks or solve problems exactly like the ones addressed during instruction, then deeper learning is not needed. For example, if someone’s job calls for adding lists of numbers accurately, that individual needs to learn to become proficient in using the addition procedure but does not need deeper learning about the nature of number and number theory that will allow transfer to new situations that involve the application of mathematical principles. As discussed in the previous chapter, today’s technology has reduced demand for such routine skills (e.g., Autor, Levy, and Murnane, 2003). Success in work and life in the 21st century is associated with cognitive, intrapersonal, and interpersonal competencies that allow individuals to adapt effectively to changing situations rather than to rely solely on well-worn procedures.
When the goal is to prepare students to be able to be successful in solving new problems and adapting to new situations, then deeper learning is called for. Calls for such 21st century skills as innovation, creativity, and creative problem solving can also be seen as calls for deeper learning—helping students develop transferable knowledge that can be applied to solve new problems or respond effectively to new situations. Before turning to a discussion of the relationship between deeper learning and 21st century competencies in terms of theories and research on learning and knowing and the implications for transfer, we briefly discuss some of the rich history of work on the nature and extent of transfer.
Brief Historical Overview of Theory and Research on Transfer
Transfer was one of the first topics on the research agendas of both psychology and education, and it has remained as perhaps the central topic in the research on learning and instruction for more than 100 years. Research to date suggests that despite our desire for broad forms of transfer, knowledge does not transfer very readily, but it also illuminates instructional conditions that support forms of transfer that are desirable and attainable.
Specific transfer is the idea that learning A affects one’s learning of B only to the extent that A and B have elements in common. For example, learning Latin may help someone learn Spanish solely because some of the vocabulary words are very similar and the verb conjugations are very similar. In contrast, general transfer is the idea that learning A affects one’s learning of B because learning A strengthens general characteristics or
knowledge in the learner that are broadly relevant (such as mental discipline or general principles). On the general transfer side of the controversy was the doctrine of formal discipline, which held that learning certain school subjects such as Latin and geometry would improve the mind in general (i.e., teach proper habits of mind) and thereby improve learning and performance in other unrelated subjects. On the specific transfer side of the controversy was E.L. Thorndike, largely recognized as the founder of educational psychology, who sought to put the issue to an empirical test. In a famous set of early studies, Thorndike and Woodworth (1901) found that students who were taught a cognitive skill showed a large improvement on the taught tasks but not on other tasks. Thorndike was able to claim strong support for specific rather than general transfer: “Improvement in any single mental function rarely brings about equal improvement in any other function, no matter how similar” (Thorndike, 1903, p. 91).
This was not a good outcome for those dedicated to helping students develop the ability to exhibit general transfer—that is, to apply what they have learned in one situation to a novel situation. Subsequent work by Judd (1908) offered some hope by showing that transfer to new situations depended on the instructional method used during initial learning, with some instructional methods supporting transfer to new situations and others not. An important aspect of Judd’s finding is that transfer was restricted to new situations that required the same general principles as required in the original task, although it could be applied to situations requiring different behaviors.
Judd’s finding has been replicated in many contexts. For example, Singley and Anderson (1989) report on an experiment designed to study the acquisition and transfer of skills in text editing. A group of 24 young women (aged 18-30) from a secretarial school were first taught to use either one or two line editors (text editing software used to change individual lines of text) and then a screen editor (text editing software used to scroll throughout a page of text), while control groups spent similar amounts of time either learning and using one of the screen editors or simply typing a manuscript. The authors observed positive transfer, both from one line editor to the next and from the line editors to the screen editor, as indicated by reductions in total learning time, keystrokes, residual errors, and other measures in comparison to the control groups. They proposed that the very high level of transfer from one line editor to the next line editor was due to the fact that, although the surface features of the commands used in the two editors were different, the underlying principles were nearly identical. In addition, they proposed that the moderate level of transfer from the line editors to the screen editor reflected the fact that the procedures used in the two line editors are largely different from those used by the screen editor. Nevertheless, the two line editors and the screen editor do share several
decision rules, enabling the moderate level of transfer. It is important to note that this research examined transfer within a single subject or topic area—text editing. Research to date has not found evidence of transfer across subjects or disciplines.
Although there is little support in the research literature for general transfer in the broadest sense, there is encouraging evidence for what could be called “specific transfer of general principles” within a subject area or topic when effective instructional methods are used. Understanding how to promote this type of specific transfer is a continuing goal of research. Much of contemporary work continues to follow a line of thinking originally developed by the gestalt psychologists (e.g., Katona, 1942; Wertheimer, 1959) working in the first half of the 20th century. They were the first to propose a distinction between reproductive thinking (i.e., applying a previously learned procedure to solve a new problem) and productive thinking (i.e., inventing a new solution method to solve a new problem). Insight—moving from a state of not knowing how to solve a problem to a state of knowing how to solve it—is at the heart of productive thinking and was a major research theme of gestalt psychology (Duncker, 1945; Mayer, 1995). The gestaltists also emphasized the distinction between rote learning (which involved learning to blindly follow a procedure) and meaningful learning (which involved deeper understanding of the structure of the problem and the solution method), and they provided evidence that meaningful learning leads to transfer, whereas rote learning does not (Katona, 1940). For example, Wertheimer showed that in learning to solve for the area of a parallelogram, students could be taught how to apply the formula area = height × base (learning by rote), or they could be shown that they could cut off a triangle from one end and place it on the other end to form a rectangle (learning by understanding). According to Wertheimer, both kinds of instruction enabled students to perform well on problems like those given during instruction (i.e., retention tests), but only learning by understanding could promote problem solving on unusually shaped parallelograms and related nonparallelogram shapes (i.e., transfer tests).
Overall, one of the continuing goals of research and theory is to elucidate what is meant by learning with understanding—the processes that produce such learning as well as the outcomes in terms of knowledge representations—as well as how the products of such “deeper learning” processes lead to productive thinking in the context of transfer situations (see, e.g., Schwartz, Bransford, and Sears, 2005). In the next section, we consider the relationship between deeper learning and 21st century skills from the perspective of contemporary research and theory on the nature of the mental structures and cognitive processes associated with learning as well as the sociocultural nature of learning and knowing.
THE RELATIONSHIP BETWEEN DEEPER LEARNING AND COGNITIVE COMPETENCIES
To clarify the meaning of “deeper learning” and illuminate its relationship to 21st century competencies in the cognitive domain, the committee turned to two important strands of research and theory on the nature of human thinking and learning, the cognitive perspective and the sociocultural perspective, also referred to as the “situated” perspective (Greeno, Pearson, and Schoenfeld, 1996). In contrast to the differential perspective discussed in Chapter 2, which focuses on differences among individuals in knowledge or skill, the cognitive perspective focuses on types of knowledge and how they are structured in an individual’s mind, including the processes that govern perception, learning, memory, and human performance. Research from the cognitive perspective investigates the mechanisms of learning and the nature of the products—the types of knowledge and skill—that result from those mechanisms, as well as how that knowledge and skill is drawn upon to perform a range of simple to complex tasks. The goal is theory and models that apply to all individuals, accepting the fact that there will be variation across individuals in execution of the processes and in the resultant products.
The sociocultural perspective emerged in response to the perception that research and theory within the cognitive perspective was too narrowly focused on individual thinking and learning. In the sociocultural perspective, learning takes place as individuals participate in the practices of a community, using the tools, language, and other cultural artifacts of the community. From this perspective, learning is “situated” within, and emerges from, the practices in different settings and communities. A community may be large or small and may be located inside or outside of a traditional school context. It might range, for example, from colleagues in a company’s Information Technology department to a single elementary school classroom or a global society of plant biologists.
Such research has important implications for how academic disciplines are taught in school. From the sociocultural perspective, the disciplines are distinct communities that engage in shared practices of ongoing knowledge creation, understanding, and revision. It is now widely recognized that science is both a body of established knowledge and a social process through which individual scientists and communities of scientists continually create, revise, and elaborate scientific theories and ideas (Polanyi, 1958; National Research Council, 2007). In one illustration of the social dimensions of science, Dunbar (2000) found that scientists’ interactions with their peers, particularly how they responded to questions from other scientists, influenced their success in making discoveries.
The idea that each discipline is a community with its own culture, language, tools, and modes of discourse has influenced teaching and learning. For example, Moje (2008) has called for reconceptualizing high school literacy instruction to develop disciplinary literacy programs, based on research into what it means to write and read in mathematics, history and science and what constitutes knowledge in these subjects. Moje (2008) argues that students’ understanding of how knowledge is produced in the subject areas is more important than the knowledge itself.
Sociocultural perspectives are reflected in new disciplinary frameworks and standards for K-12 education. In science, for example, A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (hereafter referred to as the NRC science framework; National Research Council, 2012) calls for integrated development of science practices, crosscutting concepts, and core ideas. The Common Core State Standards in English language arts (Common Core State Standards Initiative, 2010a) reflect an integrated view of reading, writing, speaking/listening, and language and also respond to Moje’s (2008) call for disciplinary literacy by providing separate English language arts standards for history and science. Based on the view of each discipline as a community engaged in ongoing discourse and knowledge creation, the NRC science framework and the standards in English language arts and mathematics include expectations for learning of intrapersonal and interpersonal competencies along with cognitive competencies (see Chapter 5 for further discussion).
In the committee’s view, and informed by both perspectives, the link between deeper learning and 21st century competencies lies in the classic concept of transfer—the ability to use prior learning to support new learning or problem solving in culturally relevant contexts. We define deeper learning not as a product but as processing—both within individual minds and through social interactions in a community—and 21st century competencies as the learning outcomes of this processing in the form of transferable knowledge and skills that result. The transferable knowledge and skills encompass all three domains of competency: cognitive, intrapersonal, and interpersonal, in part reflecting the sociocultural perspective of learning as a process grounded in social relationships.
To support our proposed definitions of deeper learning and 21st century competencies, we first draw on concepts and principles derived from work in cognitive psychology. Based on this review of the research, we describe the nature of deeper learning and briefly discuss instruction that supports deeper learning and transfer (we elaborate on teaching for transfer in Chapters 5 and 6).
FIGURE 4-1 An information processing model memory.
SOURCE: Mayer, Heiser, and Lonn (2001). Copyright 2001 by the American Psychological Association. Reproduced with permission. The use of APA information does not imply endorsement by APA.
Components of Cognitive Architecture1
One of the chief theoretical advances to emerge from research and theory is the notion of cognitive architecture—the information processing system that determines the flow of information and how it is acquired, stored, represented, revised, and accessed in the mind. Figure 4-1 shows the main components of this architecture. Research has identified the distinguishing characteristics of the various types of memory shown in Figure 4-1 and the mechanisms by which they interact with each other.
Working memory is what people use to process and act on information immediately before them (Baddeley, 1986). Working memory is a conscious system that receives input from memory buffers associated with the various sensory systems. There is also considerable evidence that working memory can receive input from the long-term memory system.
The key variable for working memory is capacity—how much information it can hold at any given time. Controlled (also defined as conscious) human thought involves ordering and rearranging ideas in working memory and is consequently restricted by the finite capacity of working memory. Simply stated, working memory refers to the currently active portion of long-term memory. But there are limits to such activity, and these limits are governed primarily by how information is organized. Although few people can remember a randomly generated string of 16 digits, anyone with a slight knowledge of American history is likely to be able to recall the string 1492-1776-1865-1945. This is just one example of an important concept:
1This section of the chapter draws heavily on National Research Council (2001, pp. 65-68).
namely, that knowledge stored in long-term memory can have a profound effect on what appears, at first glance, to be the capacity constraint in working memory.
Long-term memory contains two distinct types of information—semantic information about “the way the world is” and procedural information about “how things are done.” Unlike working memory, long-term memory is, for all practical purposes, an effectively limitless store of information. It therefore makes sense to try to move the burden of problem solving from working memory to long-term memory. What matters most in learning situations is not the capacity of working memory—although that is a factor in speed of processing—but how well one can evoke the knowledge stored in long-term memory and apply it to address information and problems in the present.
Contents of Memory
Contemporary theories also characterize the types of cognitive content that are processed by the architecture of the mind. The nature and organization of this content is extremely critical for understanding how people answer questions and solve problems, and how they differ in this regard as a function of the conditions of instruction and learning. An important distinction in cognitive content is between domain-general knowledge, which is applicable to a range of situations, and domain-specific knowledge, which is relevant to a particular problem area.
Domain-General Knowledge and Problem-Solving Processes
Cognitive research has shown that general problem-solving procedures, not specific to a particular domain of knowledge, are generally slow and inefficient. Newell and Simon (1972) developed a computer program to test such general procedures, known as “weak methods,” identifying their limitations as follows:
- Hill climbing: One solves a problem by taking one step at a time toward the overarching goal or task. This approach is inflexible and may be inefficient, as selecting whatever step takes one uphill (or in a particular direction) may cause the problem solver to climb a foothill, ignoring the much more efficient procedure of going around it. More sophisticated problem-solving strategies, such as
those used by expert chess players, require one to look ahead many steps to see potential problems well in advance and avoid them.
- Means-ends analysis: One solves a problem by considering the obstacles that stand between the initial problem state and the goal state. The problem solver then identifies subgoals related to the elimination of each these obstacles. When all of the subgoals have been achieved (all of the obstacles have been eliminated), then the main goal of interest has been achieved. Because the subgoals have been identified through a focus on the main goal, means-ends analysis can be viewed as a strategy in which the long-range goal is always kept in mind to guide problem solving. It is not as nearsighted as other search techniques, like hill climbing.
- Analogy: One solves a problem by using the solution of a similar problem. However, evidence shows that, generally, people who have learned to solve a first problem are not better at solving a second problem analogous to the first. Even when given explicit instructions about the relationship between the two problems, individuals do not always find it easier to solve the second problem.
- Trial and error: One solves a problem by randomly trying out solutions until one has reached the goal. Trial-and-error approaches can be very inefficient, as many of the random solutions may be incorrect, and there is no boundary to narrow the search for possible solutions.
Problem solvers confronted by a problem outside their area of expertise use these weak methods to try to constrain what would otherwise be very large search spaces when they are solving novel problems. In most situations, however, learners are expected to use strong methods—relatively specific algorithms particular to the domain that will make it possible to solve problems efficiently. Strong methods, when available, find solutions with little or no search. For example, someone who knows calculus can find the maximum of a function by applying a known algorithm (taking the derivative and setting it equal to zero). As discussed further below, experts are able to quickly solve novel problems within their domain of expertise because they can readily retrieve relevant knowledge, including the appropriate, strong methods to apply. Paradoxically, although one of the hallmarks of expertise is access to a vast store of strong methods in a particular domain, both children and scientists fall back on their repertoire of weak methods when faced with truly novel problems (Klahr and Simon, 1999).
Knowledge Organization: Schemas and Expert-Novice Differences2
Although weak methods remain the last resort when one is faced with novel situations, people generally strive to interpret situations so that they can apply schemas—previously learned and somewhat specialized techniques (i.e., strong methods) for organizing knowledge in memory in ways that are useful for solving problems. Schemas help people interpret complex data by weaving them into sensible patterns. A schema may be as simple as “Thirty days hath September” or more complex, such as the structure of a chemical formula. Schemas help move the burden of thinking from working memory to long-term memory. They enable competent performers to recognize situations as instances of problems they already know how to solve; to represent such problems accurately, according to their meaning and underlying principles; and to know which strategies to use to solve them.
The existence of problem-solving schemas has been demonstrated in a wide variety of contexts. Extensive research shows that the ways students mentally “represent” (form a mental model of) the information given in a math or science problem or in a text that they read depends on the organization of their existing knowledge. As learning occurs, increasingly well-structured and qualitatively different organizations of knowledge develop. These structures enable individuals to build a representation or mental model that guides problem solution and further learning, avoid trial-and-error solution strategies, and formulate analogies and draw inferences that readily result in new learning and effective problem solving (Glaser and Baxter, 1999). The impact of schematic knowledge is powerfully demonstrated by research on the nature of expertise.
Research conducted over the past five decades has generated a vast body of knowledge about how people learn the content and procedures of specific subject domains. Researchers have probed deeply the nature of expertise and how people acquire large bodies of knowledge over long periods of time. Studies have revealed much about the kinds of mental structures that support problem solving and learning in various domains ranging from chess to physics; what it means to develop expertise in a domain; and how the thinking of experts differs from that of novices.
The notion of expertise is inextricably linked with subject-matter domains: experts must have expertise in something. Research on how people develop expertise has provided considerable insight into the nature of thinking and problem solving. Although every person cannot be expected to become an expert in a given domain, findings from cognitive science about the nature of expertise can shed light on what successful learning looks like and guide the development of effective instruction and assessment.
2This section of the chapter draws heavily on National Research Council (2001, pp. 70-73).
What distinguishes expert from novice performers is not simply general mental abilities, such as memory or fluid intelligence, or general problem-solving strategies. Experts have acquired extensive stores of knowledge and skill in a particular domain, and perhaps more significantly, they have organized this knowledge in ways that make it readily retrievable and useful.
In fields ranging from medicine to music, studies of expertise have shown repeatedly that experts commit to long-term memory large banks of well-organized facts and procedures, particularly deep, specialized knowledge of their subject matter (Chi, Glaser, and Rees, 1982; Chi and Koeske, 1983). Most important, they have efficiently coded and organized this information into well-connected schemas. These methods of encoding and organizing help experts interpret new information and notice features and meaningful patterns of information that might be overlooked by less competent learners. These schemas also enable experts, when confronted with a problem, to retrieve the relevant aspects of their knowledge.
Of particular interest to researchers is the way experts encode, or chunk, information into meaningful units based on common underlying features or functions. Doing so effectively moves the burden of thought from the limited capacity of working memory to long-term memory. Experts can represent problems accurately according to their underlying principles, and they quickly know when to apply various procedures and strategies to solve them. They then go on to derive solutions by manipulating those meaningful units. For example, chess experts encode mid-game situations in terms of meaningful clusters of pieces (Chase and Simon, 1973).
The knowledge that experts have cannot be reduced to sets of isolated facts or propositions. Rather, their knowledge has been encoded in a way that closely links it with the contexts and conditions for its use. Because the knowledge of experts is “conditionalized,” they do not have to search through the vast repertoire of everything they know when confronted with a problem. Instead, they can readily activate and retrieve the subset of their knowledge that is relevant to the task at hand (Simon, 1979; Glaser, 1992). These and other related findings suggest that teachers should place more emphasis on the conditions for applying the facts or procedures being taught, and that assessment should address whether students know when, where, and how to use their knowledge.
Practice and Feedback3
Every domain of knowledge and skill has its own body of concepts, factual content, procedures, and other items that together constitute the knowledge of that field. In many domains, including areas of literature,
3This section of the chapter draws heavily on National Research Council (2001, pp. 84-87).
history, mathematics, and science, this knowledge is complex and multifaceted, requiring sustained effort and focused instruction to master. Developing deep knowledge of a domain such as that exhibited by experts, along with conditions for its use, takes time and focus and requires opportunities for practice with feedback.
Whether considering the acquisition of some highly specific piece of knowledge or skill such as the process of adding two numbers, or some larger schema for solving a mathematics or physics problem, certain laws of skill acquisition always apply. The first of these is the power law of practice: acquiring skill takes time, often requiring hundreds or thousands of instances of practice in retrieving a piece of information or executing a procedure. This law operates across a broad range of tasks, from typing on a keyboard to solving geometry problems (Rosenbloom and Newell, 1987). According to the power law of practice, the speed and accuracy of performing a simple or complex cognitive operation increases in a systematic nonlinear fashion over successive attempts (see Figure 4-2). This pattern is characterized by an initial rapid improvement in performance, followed by subsequent and continuous improvements that accrue at a slower and slower rate.
The power law of practice is fully consistent with theories of cognitive skill acquisition, according to which individuals go through different stages in acquiring the specific knowledge associated with a given cognitive skill (e.g., Anderson, 1982). Early on in this process, performance requires effort because it is heavily dependent on the limitations of working memory. Individuals must create a representation of the task they are supposed to perform, and they often verbally mediate or “talk their way through the task” while it is being executed. Once the components of the skill are well represented in long-term memory, the heavy reliance on working memory, and the problems associated with its limited capacity, can be bypassed. As a consequence, exercise of the skill can become fluent and then automatic. In the latter case, the skill requires very little conscious monitoring, and thus mental capacity is available to focus on other matters. Evidence indicates that with each repetition of a cognitive skill, as in accessing a concept in long-term memory from a printed word, retrieving an addition fact, or applying a schema for solving differential equations, some additional knowledge strengthening occurs that produces continual small improvements.
Practice, however, is not enough to ensure that a skill will be acquired. The conditions of practice are also important. The second major law of skill acquisition involves knowledge of results. Individuals acquire a skill much more rapidly if they receive feedback about the correctness of what they have done. If incorrect, they need to know the nature of their mistake. It was demonstrated long ago that practice without feedback produces little learning (Thorndike, 1927). One of the persistent dilemmas in education
is that students often spend time practicing incorrect skills with little or no feedback. Furthermore, the feedback they ultimately receive is often neither timely nor informative. For the less able student, unguided practice (e.g., homework in math) can be practice in doing tasks incorrectly.
The timing and quality of feedback influences its effectiveness in speeding acquisition of skills or knowledge (Pashler et al., 2005; Shute, 2008). The optimal timing of feedback appears to differ depending on the type and complexity of the learning task and the characteristics of the learner. For example, immediate feedback can quickly prevent further incorrect practice, but it also has potential limitations, including posing a threat to motivation and reducing opportunities for learners to correct their own errors and develop self-regulated learning skills. There is growing evidence that feedback that explains why the practice is incorrect is more valuable for learning than feedback that simply flags errors (Roscoe and Chi, 2007; Shute, 2008; National Research Council, 2011a). The value of explanatory feedback has been demonstrated through research conducted in both digital and nondigital learning environments. For example, Moreno and Mayer (2005) compared two different versions of an interactive science learning game in which students traveled to different planets with different environmental conditions and were asked to design a plant that could survive in these conditions. The authors found that students who received explanatory feedback performed significantly better than did students who received only corrective feedback on a test designed to measure both retention of the targeted botany concepts and transfer of these concepts to new problems of plant design based on the same general principles.
The Nature of Deeper Learning
The review of research thus far in this chapter allows us to more clearly describe the nature of deeper learning. First, the history of research on transfer suggests that there are limits to how far the knowledge and skills developed through deeper learning can transfer. Transfer is possible within subject area or domain of knowledge, when effective instructional methods are used. Second, the research on expertise suggests that deeper learning involves the development of well-organized knowledge in a domain that can be readily retrieved to apply (transfer) to new problems in that domain. Third, the research suggests that deeper learning requires extensive practice, aided by explanatory feedback that helps learners correct errors and practice correct procedures, and that multimedia learning environments can provide such feedback. Fourth, the work of the gestalt psychologists discussed above allows us to distinguish between rote learning and meaningful learning (or deeper learning). Meaningful learning (which develops deeper
understanding of the structure of the problem and the solution method) leads to transfer, while rote learning does not (Katona, 1940).
Building on the research of the Gestalt psychologists, we can distinguish between different types of tests and the learning they measure. Retention tests are designed to assess learners’ memory for the presented material using recall tasks (e.g., “What is the definition of deeper learning?”) or recognition tasks (e.g., “Which of the following is not part of the definition of deeper learning? A. learning that facilitates future learning, B. learning that facilitates future problem solving, C. learning that promotes transfer, D. learning that is fun.”). While retention and recognition tests are often used in educational settings, experimental psychologists use transfer tests to assess learners’ ability to use what they learned in new situations to solve problems or to learn something new (e.g., “Write a transfer test item to evaluate someone’s knowledge of deeper learning.”).
Although using the senses to attend to relevant information may be all that is required for success on retention tasks, success on transfer tasks requires deeper processing that includes organizing new information and integrating it with prior knowledge in one’s mind (see Figure 4-1). This deeper cognitive process develops 21st century skills—knowledge in a learner’s long-term memory that can be used in new situations.
Results from the two different types of assessments can be used to distinguish between three different types of learning outcomes—no learning, rote learning, and meaningful learning (see Table 4-1; also Mayer, 2010). No learning is indicated by poor performance on retention and transfer tests. Rote learning is indicated by good retention performance and poor transfer performance. Meaningful learning (which also could be called deeper learning) is indicated by good retention performance and good transfer performance. Thus the distinguishing feature of meaningful learning (or deeper learning) is the learner’s ability to transfer what was learned to new situations.
TABLE 4-1 Three Types of Learning Outcomes
|Type of Outcome||Retention Performance||Transfer Performance|
|Meaningful (deeper) learning||Good||Good|
SOURCE: R.E. Mayer, Applying the science of learning, 1st edition, © 2010. Reprinted (2010) by permission of Pearson Education, Inc., Upper Saddle River, NJ.
Components of Deeper Learning
Researchers have characterized the suite of knowledge and abilities that are used in the process of deeper learning in various ways. For example, when Anderson et al. (2001) updated Bloom’s 1956 taxonomy of learning objectives, they included three types of knowledge and skills: (1) knowledge (e.g., facts and concepts); (2) skills (e.g., procedures and strategies); and (3) attitudes (e.g., beliefs). In Chapter 2, we proposed that knowledge and skills can be divided into three broad domains of competence: cognitive, intrapersonal, and interpersonal.
Mayer (2011a) suggested that deeper learning involves developing an interconnected network of five types of knowledge:
- Facts, statements about the characteristics or relationships of elements in the universe;
- Concepts, which are categories, schemas, models, or principals;
- Procedures, or step-by-step processes;
- Strategies, general methods; and
- Beliefs about one’s own learning.
Earlier in this chapter, we noted that mentally organizing knowledge helps an individual to quickly identify and retrieve the relevant knowledge when trying to solve a novel problem (i.e., when trying to transfer the knowledge). In light of these research findings, Mayer (2010) proposed that the way in which a learner organizes these five types of knowledge influences whether the knowledge leads to deeper learning and transfer. For example, factual knowledge is more likely to transfer if it is integrated, rather than existing as isolated bits of information, and conceptual knowledge is more likely to transfer if it is mentally organized around schemas, models, or general principles. As the research on expertise and the power law of practice would indicate, procedures that have been practiced until they become automatic and embedded within long-term memory are more readily transferred to new problems than those that require much thought and effort. In addition, specific cognitive and metacognitive strategies (discussed later in this chapter) promote transfer. Finally, development of transferable 21st century skills is more likely if the learner has productive beliefs about his or her ability to learn and about the value of learning—a topic we return to later, in the section on the intrapersonal domain.
Table 4-2 outlines the cognitive processing of the five types of integrated knowledge and dispositions that, working closely together, support deeper learning and transfer.
Deeper learning involves coordinating all five types of knowledge. The learner acquires an interconnected network of specific facts, automates
TABLE 4-2 What Is Transferable Knowledge?
|Type of Knowledge||Format or Cognitive Processing|
|Factual||Integrated, rather than separate facts|
|Conceptual||Schemas, models, principles|
|Procedures||Automated, rather than effortful|
|Strategies||Specific cognitive and metacognitive strategies|
|Beliefs||Productive beliefs about learning|
SOURCE: Adapted from Mayer (2010).
procedures, refines schemas and mental models, and refines cognitive and metacognitive strategies, while at the same time developing productive beliefs about learning. Through this process, the learner develops transferable knowledge, which encompasses not only the facts and procedures that support retention but also the concepts, strategies, and beliefs needed for success in transfer tasks. We view these concepts, thinking strategies, and beliefs as 21st century skills.
This proposed model of transferable knowledge reflects the research on development of expertise, which, as noted above, has distinguished differences in the knowledge of experts and novices in domains such as physics, chess, and medicine (see Table 4-3). Novices tend to store facts as isolated units, whereas experts store them in an interconnected network. Novices tend to create categories based on surface features, whereas experts create categories based in structural features. Novices need to expend conscious effort in applying procedures, whereas experts have automated basic procedures, thereby freeing them of the need to expend conscious effort in applying them. Novices tend to use general problem-solving strategies such as means-ends analysis, which require a backward strategy starting from the goal, whereas experts tend to use specific problem-solving strategies tailored to specific kinds of problems in a domain, which involve a forward strategy starting from what is given. Finally, novices may hold unproductive beliefs, such as the idea that their performance depends on ability, whereas
TABLE 4-3 Expert-Novice Differences on Five Kinds of Knowledge
SOURCE: Adapted from Mayer (2010).
experts may hold productive beliefs, such as the idea that if they try hard enough they can solve the problem. In short, analysis of learning outcomes in terms of five types of knowledge has proven helpful in addressing the question of what expert problem solvers know that novice problem solvers do not know.
AN ILLUSTRATION OF DEEPER LEARNING AND THE DEVELOPMENT OF 21ST CENTURY COMPETENCIES
Before turning to discussions of deeper learning and 21st century competencies in the intrapersonal and interpersonal domains, we offer a description of a learning environment designed to develop mathematics competencies. Although the instruction focused on knowledge of high school mathematics, the teaching practices used to advance this goal led to development of intrapersonal and interpersonal competencies as well. We offer this case as illustrative (not definitive) of how learning and instruction in traditional school subjects might be organized in ways that produce multiple forms of transferable knowledge and skill (additional examples are provided in Chapter 5).
Our example is derived from Boaler and Staples’ (2008) 5-year longitudinal study of approximately 700 students at three high schools. Railside was an urban, ethnically diverse school, where 30 percent of students were English language learners and 30 percent of students qualified for free or reduced meals. Hilltop was a more rural school where approximately half of the students were Latino and half white, 20 percent of students were English language learners, and 20 percent qualified for free or reduced meals. Greendale was a predominantly white school in a small coastal community, with no English language learners, and only 10 percent of students qualifying for free or reduced meals. The sample of schools was chosen intentionally to allow the researchers to observe different mathematics teaching approaches, and the research team gathered a wide range of data over 4 years, including videotapes of classroom activities, assessments of mathematics content, and interviews with students and teachers.
The mathematics teachers at Railside worked collaboratively to develop and implement a mixed-ability curriculum in algebra and geometry classes and made more modest changes to advanced algebra classes. They had high expectations for all students and engaged them in a common, cognitively challenging curriculum. Students spent most of their time working together small, mixed-ability groups to address complex problems. Students at the other two high schools experienced more traditional mathematics instruction, including teacher lectures, whole-class, question-and-answer sessions, and individual practice solving relatively short, closed-ended problems.
At the beginning of the study, when incoming freshmen at all three schools took an assessment of middle school mathematics knowledge, Railside students scored significantly lower than students from the other two schools. Nevertheless, all Railside students were placed in algebra classes, with a curriculum organized around themes, such as “What is a linear function?” The teachers restructured the traditionally rigid sequence of mathematics classes so that students could take two courses within a single year (e.g., algebra and geometry). They also implemented many teaching practices designed to create a new culture of learning within the algebra classrooms. For example, teachers explicitly and publicly valued many different dimensions of mathematical work, recognized the intellectual contributions of students within a group who might otherwise be thought of as low status, and modeled for students the importance of asking good questions. The teachers conveyed to the students that there were many different methods and paths to solve the complex problems and required students to justify their answers.
One important teaching practice focused on encouraging students to be responsible for each other’s mathematics learning. Teachers did this in several ways. First, when placing students into groups, they assigned them to particular roles—such as facilitator, team captain, recorder, or resource manager—to convey the idea that all students have important contributions to make. As they circulated around the classroom, teachers frequently emphasized the different roles, for example, by reminding facilitators to help group members check their answers or show their work. In addition, the teachers encouraged students to be responsible for each other’s learning through their assessment practices, which included, at times, assigning grades based on the quality of a group’s conversations. At other times, teachers asked one member of the group a question and, if that group member could not answer, gave the group some time to help that member find the solution (without providing hints or the answer, so that the group members were required to struggle through to the answer).
At the end of each of school year, all students took content-focused assessments designed by researchers to include topics that had been addressed across the three different schools and teaching approaches (algebra at the end of year 1, geometry at the end of year 2, and advanced algebra and geometry at the end of year 3). In addition, the researcher administered open-ended project assessments in each year of the study, with longer, more applied problems that students worked on in groups. By the end of year 1, the Railside students were approaching comparable levels in algebra to students at the other two schools. By the end of year 2, the Railside students’ scores were significantly higher than those of the students in the traditional mathematics classes. At the end of year 3, the Railside students’ scores were higher, but not significantly so (perhaps because the year 3 curriculum
had not been developed as much by the teachers). In year 4, 41 percent of seniors at Railside were enrolled in calculus, compared with approximately 27 percent in the two other schools.
Railside students also scored higher than students at the other two schools on the California Standards test, a curriculum-aligned test, although they did not do as well on the CAT 6, a standardized state test, perhaps because that test requires strong English language skills and cultural knowledge. In addition, the Railside approach was successful at improving equity. Significant disparities in the mathematics achievement of incoming white, black, and Latino students at Railside disappeared over the course of the study period, although achievement differences between different ethnic groups continued at the other two schools.
These findings begin to illuminate both the process of deeper learning and its role in developing transferable skills and knowledge. Clearly, the innovative approach led to gains in cognitive competencies in mathematics. At the same time, interview data showed that students developed positive dispositions towards mathematics and conscientiousness in addressing mathematics problems—important intrapersonal competencies. For example, 84 percent of Railside students agreed with the statement, “Anyone can be really good at math if they try,” compared to 52 percent of students in the traditional classes at the other two schools. Data from the videotaped project assessments showed that Railside students persisted in working through difficult problems for longer time periods than students from the other two schools. Railside students also gained important interpersonal skills, learning to value group work not only for how it aided their own learning but also for helping others. In interviews, they expressed enjoyment in helping others and did not describe others as smart or dumb, slow or quick. Although the focus of their conversations was on mathematics, they learned to appreciate the different perspectives, insights, methods, and approaches offered by students from different cultures and circumstances.
THE INTRAPERSONAL DOMAIN4
The model of the suite of knowledge and skills developed through deeper learning shown in Table 4-2 above (Mayer, 2010) includes intrapersonal facets—specifically, productive beliefs about learning—as well as cognitive dimensions. Here, we further explore the intrapersonal dimensions of learning.
The intrapersonal domain encompasses a broad range of competencies that reside within an individual and operate across a variety of different life contexts and situations, including learning situations. We have
4This section of the chapter draws heavily on National Research Council (2001, pp. 88-89).
proposed in Chapter 2 that this domain includes three clusters of 21st century competencies:
- Intellectual openness (aligned with the personality factor of openness to experience), including such skills as flexibility, adaptability, artistic and cultural appreciation, and personal and social responsibility
- Work ethic (aligned with the personality factor of conscientiousness), including such skills as initiative and self-direction, responsibility, Type 1 self-regulation (metacognition, including forethought, performance, and self-reflection), and perseverance
- Core self-evaluation (aligned with the personality factor of neuroticism and its opposite, emotional stability), including such skills as Type 2 self-regulation (self-monitoring, self-evaluation, self-reinforcement), and physical and psychological health
Below, we discuss research and theory by investigating how these competencies support learning, including evidence suggesting that they support deeper learning and transfer. We also briefly describe the broader construct of self-regulation and research in child and adolescent development and economics that suggest that competence in self-regulation transfers across a variety of life situations.
The Role of Beliefs and Motivation in Learning
In our discussion of the cognitive domain above, we noted that motivation helps learners to mentally organize and integrate information in the cognitive processing that is central to deeper learning (this is sometimes referred to as “generative processing”). We also argued that productive beliefs about learning are an essential component of transferable knowledge. Here, we explore further how beliefs and motivation support deeper learning.
The beliefs students hold about learning can significantly affect learning and performance (e.g., Dweck and Leggett, 1988). For example, many students believe, on the basis of their typical classroom and homework assignments, that any math problem can be solved in 5 minutes or less, and if they cannot find a solution in that time, they will give up. Many young people and adults also believe that talent in mathematics and science is innate, which gives them little incentive to persist if they do not understand something in these subjects immediately. Conversely, people who believe they are capable of making sense of unfamiliar things often succeed because they invest more sustained effort in doing so.
A recent review of research on social-psychological interventions designed to change students’ beliefs and feelings of self-efficacy as learners
provides evidence that motivation and related intrapersonal skills enhance deeper learning (Yaeger and Walton, 2011). The authors found that relatively brief interventions can lead to large and sustained gains in student achievement, as students develop durable, transferable intrapersonal skills and apply them to new learning challenges in a positive, self-reinforcing cycle of academic improvement.
Some of the experiments target students’ “attributions”—how they explain the causes of events and experiences. Research in social psychology shows that if students attribute poor school performance to traits they view as fixed (such as general low intelligence or a more specific lack of aptitude in mathematics), they will not invest time and effort to improve their performance. This leads to an “exacerbation cycle” of negative attributions and poor performance (Storms and Nisbett, 1970).
Wilson and Linville (1982, 1985) studied a brief intervention designed to change attributions among college freshmen. They brought two groups of struggling freshmen into the laboratory to view videos of upperclassmen discussing their transition to the college. In the videos viewed by the experimental group, upperclassmen said that their grades were low at first, due to transient factors such as a lack of familiarity with the demands of college, but that their grades improved with time. In the videos viewed by the control group, upperclassmen talked about their academic and social interests but did not mention first-year grades. One year later, students in the treatment group had earned significantly higher grade point averages (0.27 percent higher) than students in the control group, and the effect increased over the following semesters. Ultimately, students in the treatment group were 80 percent less likely to drop out of college than the control group.
In another example, Blackwell, Trzesniewski, and Dweck (2007) studied an intervention designed to change attributions among low-income minority seventh-grade students in an urban school. In an 8-week period at the beginning of the school year, the students took part in eight workshops on brain function and study skills. Students in the experimental group were taught that the brain can get stronger when a person works on challenging tasks, while those in the control group learned only study skills. At the end of the academic year, the students in the experimental group earned significantly higher mathematics grades than those in the control group (a mean increase of 0.30 grade points), reversing the normal pattern of declining mathematics grades over the course of seventh grade. Noting that the effectiveness of interventions targeting attributions has been replicated with different student populations, Yaeger and Walton (2011) observe that these studies support the hypothesis that changes in attributions can lead to a positive, self-reinforcing cycle of improvement. Students who attribute a low grade to transitory factors, such as a temporary lack of effort, rather than to a lack of general intelligence or mathematics ability, are more
motivated to work harder in their classes. This leads to improved grades, which, in turn, reinforce students’ view that they can succeed academically and make them less likely to attribute any low grades to factors beyond their control.
Other experiments are designed to reduce “stereotype threat,” the worry that one is perceived as having low intelligence as a member of a stereotyped group, which has been shown to negatively affect academic performance. Yaeger and Walton (2011) describe an intervention based on self-affirmation theory, which posits that people who reflect on their positive attributes will view negative events as less threatening, experience less stress, and function more effectively than they otherwise would. Cohen et al. (2006, 2009) asked white and black seventh-grade students to complete a brief, 15-20-minute writing exercise at the beginning of the school year. The experimental group wrote about why two or three values were personally important to them, while the control group wrote about values that were not personally important. By the end of the first semester, black students in the experimental group had significantly higher grade point averages than their peers in the control group, reducing the black-white achievement gap by about 40 percent. With a few more of these exercises, the black students’ gain relative to the control group persisted for 2 years.
These brief interventions appear to work by engaging students as active participants. For example, when students write about values that are important, they are actually generating the self-affirmation intervention. Although they are intentionally brief, to avoid conveying to students that they need intensive help or remediation, the interventions “can induce deep processing and prepare students to transfer the content to new settings” (Yaeger and Walton, 2011, p. 284). The study findings showing that the interventions have led to changes in students’ academic trajectories demonstrate transfer of students’ learning to new school or college assignments.
The Importance of Metacognition
In his book on unified theories of cognition, Newell (1990) points out that there are two layers of problem solving—applying a strategy to the problem at hand, and selecting and monitoring that strategy. Good problem solving, Newell observed, often depends as much on the selection and monitoring of a strategy as on its execution. The term metacognition (literally “thinking about thinking”) is commonly used to refer to the selection and monitoring processes, as well as to more general activities of reflecting on and directing one’s own thinking.
Experts have strong metacognitive skills (Hatano, 1990). They monitor their problem solving, question limitations in their knowledge, and avoid simple interpretations of a problem. In the course of learning and problem
solving, experts display certain kinds of regulatory performance such as knowing when to apply a procedure or rule, predicting the correctness or outcomes of an action, planning ahead, and efficiently apportioning cognitive resources and time. This capability for self-regulation and self-instruction enables advanced learners to profit a great deal from work and practice by themselves and in group efforts.
Studies of metacognition have shown that people who monitor their own understanding during the learning phase of an experiment show better recall performance when their memories are tested (Nelson, 1996). Similar metacognitive strategies distinguish stronger from less competent learners. Strong learners can explain which strategies they used to solve a problem and why, while less competent students monitor their own thinking sporadically and ineffectively and offer incomplete explanations (Chi et al., 1989; Chi and VanLehn, 1991).
There is ample evidence that metacognition develops over the school years; for example, older children are better than younger ones at planning for tasks they are asked to do (Karmiloff-Smith, 1979). Metacognitive skills can also be taught. For example, people can learn mental devices that help them stay on task, monitor their own progress, reflect on their strengths and weaknesses, and self-correct errors. It is important to note, however, that the teaching of metacognitive skills is often best accomplished in specific content areas since the ability to monitor one’s understanding is closely tied to domain-specific knowledge and expertise (National Research Council, 1999).
Self-Regulated Learning and Self-Regulation
Student beliefs about learning, motivation, and metacognition are all dimensions of the broader construct of self-regulated learning, which focuses on understanding how learners take an active, purposeful role in learning, by setting goals and working to achieve them.
In a recent review of the research on self-regulated learning, Wolters (2010) observes that, although there are several different models of such learning, the most prominent is that developed by Pintrich and colleagues (Pintrich, 2000, 2004). In this model, learners engage in four phases of self-regulation, not necessarily in sequential order: forethought or planning (setting learning goals); monitoring (keeping track of progress in a learning activity); regulation (using, managing, or changing learning strategies to achieve the learning goals; and reflection (generating new knowledge about the learning tasks or oneself as a learner). These phases overlap substantially with the elements of Type 1 self-regulation included in our proposed cluster of Work Ethic/Conscientiousness skills (see Table 2-2). As the learner engages in the different phases of self-regulation, he or she may
regulate one or more of several interrelated dimensions of learning, including cognition (for example, by using cognitive and metacognitive learning strategies); motivation and affect (for example, by planning to reward himself or herself after studying); learning behavior; and the learning context or environment (such as deciding where to study, and who to study with).
Comparing these dimensions of self-regulated learning with a list of 21st century skills proposed by Ananiadou and Claro (2009), Wolters found a high degree of conceptual overlap. The 21st century skills of initiation and self-direction were congruent with self-regulated learning, as the ability to set learning goals and manage the pursuit of those goals is a hallmark of a self-regulated learner. The 21st century skill of adaptability, including the ability to respond effectively to feedback, is very similar (or identical) to what the learner does in the monitoring and reflection phases of self-regulated learning. Learners who are strong in self-regulated learning are seen as particularly adept at using different forms of feedback to continue and complete learning activities. Earlier in this chapter, we noted that development of expertise requires not only extensive practice but also feedback. Accordingly, development of self-regulated learning skills should aid development of expertise in a domain.
Wolters (2010) identified a moderate degree of overlap between self-regulated learning and the interpersonal skills of collaboration and communication. He notes that research on self-regulated learning has begun to explore the interpersonal dimensions of this “intrapersonal” skill, finding that the abilities and beliefs underlying self-regulated learning are developed through social processes. In addition, self-regulated learners are effective at seeking help from peers or teachers, working in groups, and other aspects of collaboration (Newman, 2008). Wolters (2010) concluded that the conceptual similarities between 21st century skills and dimensions of self-regulated learning lend support to the critical importance of competencies such as self-direction, adaptability, flexibility, and collaboration, and suggested drawing on the self-regulated learning research to improve understanding of the 21st century skills.
The construct of self-regulated learning has been used to design instructional interventions that have improved academic outcomes among diverse populations of students, from early elementary school through college. These interventions have led to improvements in class grades and other measures of achievement in writing, reading, mathematics, and science (Wolters, 2010).
Further research is needed to more clearly define the dimensions of self-regulated learning, the relationship between this construct and 21st century skills, and how development of self-regulated learning influences academic engagement and attainment for diverse groups of students (Wolters, 2010). Longitudinal research or other research to improve our understanding of
the developmental trajectory of different dimensions of self-regulated learning, such as time management and goal-setting, would help to determine the age level at which students should begin to develop these dimensions. In addition, research is needed to develop more unified assessments of self-regulated learning. The currently available measures (using self-reports, observational, and other methods) suffer from shortcomings and are not fully aligned with current views of self-regulated learning.
Self-regulated learning is one facet of the broader skill of self-regulation, which is related to conscientiousness. Self-regulation encompasses setting and pursuing short- and long-term goals and staying on course despite internal and external challenges; it includes managing one’s emotions (Hoyle and Davisson, 2011). What an individual uses to overcome internal challenges, such as counterproductive impulses, or external challenges that may arise in different situations requires a set of strategies that, taken together, comprise self-regulation.
Research on self-regulation is growing rapidly, with hundreds of articles and five major edited volumes published since 2000 (Hoyle and Davisson, 2011). Reflecting the breadth of the construct, researchers have studied self-regulation in various life contexts, such as emotion, chronic illness, smoking, exercise, eating, and shopping (Wolters, 2010). To date, there is no consensus in the research on how to define self-regulation. In a review of 114 chapters in edited volumes, Hoyle and Davisson (2011) found that some provided no definition at all, there was no evidence of a common definition, and the same authors sometimes proposed different definitions in different chapters. Because the different definitions include a large number of behavioral variables, further research is needed to more clearly delimit the construct and to exclude variables that are not a critical element of self-regulation.
In the previous chapter, we summarized research indicating that attention, a dimension of self-regulation, is related to reading and math achievement. Attention is the ability to control impulses and focus on tasks (e.g., Raver, 2004), and plays an important role in avoiding antisocial behavior. Specifically, we noted that attention, measured at school entry, predicts later reading and mathematics achievement in elementary school (Duncan et al., 2007). In addition, children who are weak in self-regulation, as indicated by persistently high levels of antisocial behavior across the elementary school years, are significantly less likely to graduate from high school and to attend college than children who never had these problems (Duncan and Magnuson, 2011). Developmental psychologists have developed measures of self-regulation in young children that focus on the ability to delay
gratification. Longitudinal studies have found that measures of this dimension of self-regulation in early childhood predict academic and social competence in adolescence (Mischel, Shoda, and Peake, 1988; Shoda, Mischel, and Peake, 1990). Conversely, children who lacked self-regulation in early childhood are more likely at age 18 to be impulsive, to seek danger, to be aggressive, and to be alienated from others (Arsenault et al., 2000).
Given the importance of self-regulation, greater consensus on how to conceptualize this broad construct is needed. The current disagreement in the literature about how to define the foundations, process, and consequences of self-regulation poses a major barrier to the development of accurate assessments of it (Hoyle and Davisson, 2011). As we discuss in the following chapter, teaching for deeper learning and transfer begins with a model of student learning, representing the desired outcomes, and includes assessments to measure student progress toward these outcomes. Agreement on definitions is an essential first step toward teaching and learning of self-regulation.
THE INTERPERSONAL DOMAIN
The sociocultural perspective that learning is “situated” within unique social contexts and communities illuminates the importance of the interpersonal domain for deeper learning. This domain encompasses a broad range of skills and abilities that an individual draws on when interacting with others. We have proposed in Chapter 2 that it includes two skill clusters:
- Teamwork and collaboration (aligned with the personality factor of agreeableness), including such skills as communication, collaboration, teamwork, cooperation, interpersonal skills, and empathy
- Leadership (aligned with the personality factor of extroversion), including such skills as leadership and responsibility, assertive communication, self-presentation, and social influence
This preliminary taxonomy of the interpersonal domain represents an initial step toward addressing the problem of a lack of clear, agreed-upon definitions of interpersonal skills and processes. Below, we discuss the role of interpersonal skills in deeper learning, and then return to the definitional problem.
Much of what humans learn, beginning informally at birth and continuing in more structured educational and work environments, is acquired through discourse and interactions with others. For example, development of new knowledge in science, mathematics, and other disciplines is often shaped by collaborative work among peers (e.g., Dunbar, 2000). Through such interactions, individuals build communities of practice, test their own
theories, and build on the learning of others. Individuals who are using a naive strategy can learn by observing others who have figured out a more productive one. The social nature of learning contrasts with many school situations in which students are often required to work independently. Yet the display and modeling of cognitive competence through group participation and social interaction is an important mechanism for the internalizing of knowledge and skill (National Research Council, 1999).
An example of the importance of social context can be found in the 1994 work of Ochs, Jacoby, and Gonzales. They studied the activities of a physics laboratory research group whose members included a senior physicist, a postdoctoral researcher, technical staff, and predoctoral students. They found that workers’ contributions to the laboratory depended significantly on their participatory skills in a collaborative setting—that is, on their ability to formulate and understand questions and problems, to construct arguments, and to contribute to the construction of shared meanings and conclusions.
Lave and Wenger (1991) proposed that much of knowledge is embedded within shared systems of representation, discourse, and physical activity in “communities of practice” and that such communities support the development of identity—one is what one practices, to some extent. In this view, school is just one of the many contexts that can support learning. Several studies have supported the idea that knowledge and skills are developed and applied in communities of practice. For example, some researchers have analyzed the use of mathematical reasoning skills in workplace and other everyday contexts (Lave, 1988; Ochs, Jacoby, and Gonzales, 1994). One such study found that workers who packed crates in a warehouse applied sophisticated mathematical reasoning in their heads to make the most efficient use of storage space, even though they may not have been able to solve the same problem expressed as a standard numerical equation (Scribner, 1984). The rewards and meaning that people derive from becoming deeply involved in a community can provide a strong motive to learn.
Studies of the social context of learning show that, in a responsive social setting, learners observe the criteria that others use to judge competence and can adopt these criteria. Learners then apply these criteria to judge and perfect the adequacy of their own performance. Shared performance promotes a sense of goal orientation as learning becomes attuned to the constraints and resources of the environment. In school, students develop facility in giving and accepting help (and stimulation) from others. Social contexts for learning make the thinking of the learner apparent to teachers and other students so that it can be examined, questioned, and built on as part of constructive learning.
Social Dimensions of Motivation and Self-Regulated Learning
Earlier in this chapter, we discussed interventions designed to change students’ beliefs about themselves as learners and also their motivation for learning (Yaeger and Walton, 2011). Although these interventions target intrapersonal skills and attitudes as a way to enhance cognitive learning, they are based on research and theory from social psychology. The interventions are carefully designed to tap into social communities and relationships that are important and meaningful to the targeted audiences. For example, the intervention by Wilson and Linville (1982, 1985) used videos of upperclassmen to convey an important message to struggling freshmen because upperclassmen are viewed as trusted sources of information by freshmen. Similarly, we noted that the abilities and beliefs underlying self-regulated learning are developed through social processes and that self-regulated learners are effective at seeking help from peers or teachers, working in groups, and other aspects of collaboration (Newman, 2008). In Chapter 3, we observed that children lacking interpersonal skills, as reflected in persistent patterns of antisocial behavior over the elementary school years, are significantly less likely to graduate from high school and to attend college than children who never had these problems (Duncan and Magnuson, 2011). Clearly, social and interpersonal skills support deeper learning that transfers to new classes and problems, enhancing academic achievement.
IMPLICATIONS FOR INSTRUCTION
Findings from the research reviewed in this chapter have important implications for how to organize teaching and learning to facilitate deeper learning and development of transferable 21st century competencies. Here, we briefly summarize some of the implications, and in Chapter 6, we discuss in greater detail how to design instruction to support deeper learning.
As summarized by a previous NRC committee, research conducted over the past century has (National Research Council, 2001, p. 87):
clarified the principles for structuring learning so that people will be better able to use what they have learned in new settings. If knowledge is to be transferred successfully, practice and feedback need to take a certain form. Learners must develop an understanding of when (under what conditions) it is appropriate to apply what they have learned. Recognition plays an important role here. Indeed, one of the major differences between novices and experts is that experts can recognize novel situations as minor variants of situations to which they already know how to apply strong methods.
Experts’ ability to recognize familiar elements in novel problems allows them to apply (or transfer) their knowledge to solve such problems. The
research has also clarified that transfer is also more likely to occur when the person understands the underlying principles of what was learned. The models children develop to represent a problem mentally, and the fluency with which they can move back and forth among representations, are other important dimensions of transfer that can be enhanced through instruction.
The main challenge in designing instruction for transfer is to create learning experiences for learners that will prime appropriate cognitive processing during learning without overloading the learner’s information-processing system. Research on learning with multimedia tools has led to the development of the cognitive theory of multimedia learning (Mayer, 2009, 2011a), derived from the cognitive load theory (Sweller, 1999; Plass, Moreno, and Brünken, 2010). This theory posits that learners experience cognitive demands during learning, but their limited processing capacity restricts the amount of cognitive processing they can engage in at any one time. According to both theories, learning experiences may place three different types of demands on learners’ limited working memory: (1) extraneous processing, (2) essential processing, and (3) generative processing (Sweller, 1999; Mayer, 2009, 2011a; Plass, Moreno, and Brünken, 2010). Extraneous processing does not serve the learning goals and is caused by poor instructional design. Essential processing is necessary if a learner is to mentally represent the essential material in the lesson, and it is required to address the material’s complexity. Generative processing involves making sense of the material (e.g., mentally organizing it and relating it to relevant prior knowledge) and depends on the learner’s motivation to exert effort during learning.
Depending on how it is designed, instruction may lead to one of three types of cognitive processing: extraneous overload, essential overload, and generative underuse (Mayer, 2011a). If instruction creates an extraneous overload situation, the amount of extraneous, essential, and generative processing required by the instructional task exceeds the learner’s cognitive capacity for processing in working memory. An appropriate instructional goal for extraneous overload situations is to reduce extraneous processing (thereby freeing up cognitive capacity for essential and generative processing). If instruction creates an essential overload situation, the amount of essential and generative processing required by the instructional task exceeds the learner’s cognitive capacity, even though extraneous processing demands have been reduced or eliminated. An appropriate instructional goal for essential overload situations is to manage essential processing (as it cannot be cut because it is essential for the instructional objective). Finally, if instruction creates a situation of generative underuse, the learner does not engage in sufficient generative processing even though cognitive capacity is available. An appropriate instructional goal for generative underuse situations is to foster generative processing.
In Chapter 6, we discuss evidence-based instructional methods for reducing extraneous processing, managing essential processing, and promoting generative processing. That chapter describes examples of techniques that have been successful in teaching for transfer, including findings from specific educational interventions.
Deeper learning occurs when the learner is able to transfer what was learned to new situations. Research on teaching for transfer, which primarily reflects the cognitive perspective on learning, has a long history in psychology and education. This research indicates that learning for transfer requires knowledge that is mentally organized, understanding of the broad principles of the knowledge, and skills for using this knowledge to solve problems. Other, more recent research indicates that intrapersonal skills and dispositions, such as motivation and self-regulation, support deeper learning and that these valuable skills and dispositions can be taught and learned. Sociocultural perspectives on learning illuminate the potential for developing intrapersonal and interpersonal skills within instruction focused on cognitive mastery of school subjects; such perspectives provide further evidence that skills in all three domains play important roles in deeper learning and development of transferable knowledge.
- Conclusion: The process of deeper learning is essential for the development of 21st century competencies (including both skills and knowledge), and the application of transferable 21st century competencies, in turn, supports the process of deeper learning in a recursive, mutually reinforcing cycle.
In Chapter 3, the committee concluded that educational attainment is strongly predictive of positive adult outcomes in the labor market, health, and civic engagement. The research reviewed in this chapter indicates that individuals both apply and develop intertwined cognitive, intrapersonal, and interpersonal competencies in the process of deeper learning, including the learning of school subjects. Through deeper learning, individuals develop transferable 21st century competencies that facilitate improvements in academic achievement and that increase years of educational attainment. Thus the research reviewed in this chapter supports the argument that deeper learning and 21st century skills prepare young people for adult success.
At the same time, this chapter finds a lack of clear, agreed-upon definitions of specific cognitive, intrapersonal, and interpersonal competencies. This lack of shared definitions is greatest for competencies in the intrapersonal and interpersonal domains.