Learning is supported by an array of cognitive processes that must be coordinated for successful learning to occur. This chapter examines key processes that support learning. We first look at the ways that learners orchestrate processes essential to learning, such as attention, emotion regulation, and inhibition of incorrect or inappropriate responses. We then discuss memory—an essential component of most, if not all, types of learning.
The committee has drawn on both laboratory- and classroom-based research for this chapter. The research related to executive function and self-regulation draws on a mix of field- and classroom-based research from cognitive science and education involving learners of various ages, as well as on laboratory-based studies. Historically, much of the research on memory was conducted with adult populations, primarily in college settings, though younger populations have also been studied. There are historical reasons why college populations have been heavily relied on in research on memory (see Appendix C). Psychology departments recruit thousands of students in introductory psychology classes to participate in experiments, and memory has been a particularly popular subject for such experiments (Benassi et al., 2014; Pashler et al., 2007). Much of the research on memory discussed in this chapter is based on college student populations, but the committee also examined available research that included more diverse populations and learning contexts.
In Chapters 2 and 3, we discussed many of the resources on which learners draw and suggested that learners are able to coordinate these varied capacities—both consciously and unconsciously—as they are needed to meet learning challenges. How do people orchestrate their own learning? Three key ways are through metacognition, executive function, and self-regulation.
Metacognition is the ability to monitor and regulate one’s own cognitive processes and to consciously regulate behavior, including affective behavior. The term, which derives from cognitive theory, encompasses the awareness individuals have of their own mental processes (cognitive and affective) and their consequent ability to monitor, regulate, and direct their thinking to achieve a desired objective. This capacity has been studied since the early 1980s, and How People Learn: Mind Brain, Experience, and School: Expanded Edition (HPL I1) noted how important it is for educators to teach learners strategies for increasing their awareness of their learning and their capacity to direct it.
Also important is executive function, which is more frequently addressed by psychologists and neuroscientists and refers to cognitive and neural processing that involves the overall regulation of thinking and behavior and the higher-order processes that enable people to plan, sequence, initiate, and sustain their behavior toward some goal, incorporating feedback and making adjustments.
Self-regulation refers to learning that is focused by means of metacognition, strategic action, and motivation to learn. Self-regulation is seen as involving management of cognitive, affective, motivational, and behavioral components that allow the individual to adjust actions and goals to achieve desired results.
Understanding the integration and interplay of these various levels of processing is important to understanding how learners orchestrate their learning in the context of their complex cognitive and social environments. The integration and interrelation of these dimensions of processing is also critical for deeper or higher-order learning, and for the development of complex skills and knowledge such as reasoning, problem solving, and critical thinking.
The processes involved in executive function include the abilities to hold information in mind, inhibit incorrect or premature responses, and sustain or switch attention to meet a goal. These processes are highly interrelated: successful application of executive function requires that the processes operate
in coordination with one another. Many of the same processes are involved in socioemotional development, which contributes to children’s classroom success (Institute of Medicine and National Research Council, 2015). Like Kayla, the hypothetical geometry student we discussed in Chapter 3, all learners need to choose among competing interests and then sustain attention to the chosen ones long enough to make progress, hold in mind multiple pieces of information (e.g., the equation Kayla had to apply and the symbolic notation that was the target for application), manipulate them productively, and monitor their own progress.
The fundamental neural bases of executive function are relatively well known. Early research suggested that the frontal lobes were the site of this capacity (Chung et al., 2014; Damasio, 1994), but more recent neuroimaging research has shown that the various components of executive function use many areas and networks across the brain (Collette et al., 2006, Jurado and Rosselli, 2007; Marvel and Desmond, 2010). Like the positive and negative changes in prefrontal cortical thickness and connectivity with other neural structures described in Chapter 3, the component processes of executive function develop rapidly during the preschool years, continue to develop into adolescence and even beyond, and undergo characteristic changes throughout adulthood.
Executive function is a focus of intense interest—as well as targeted educational interventions (see Box 4-1)—because impaired executive function is a feature of several conditions that may negatively affect learning, including learning disabilities (both reading and mathematical disabilities); attention deficit/hyperactivity disorder, and autism. Conversely, well-developed cognitive control is correlated with numerous positive developmental outcomes, including physical health and socioeconomic status—and even absence of a criminal conviction by age 32 (Moffitt et al., 2011). Moreover, recent research suggests that executive function (indicated by behaviors such as paying attention and following rules, for example) may be a better predictor of school readiness and academic achievement than general intelligence is (e.g., Blair and Razza, 2007; Eigsti et al., 2006; McClelland et al., 2007). Interventions that target social and emotional learning may be beneficial in part because they improve executive function (Riggs et al., 2006).
Other work on executive functioning focuses on so-called “intrinsic” executive control, or a person’s ability to direct herself, change course when needed, and strategize in the absence of explicit rules to follow. For example, one study showed that 9-year-old middle-class children from Denver, Colorado, who spent more time in adult-led activities (such as piano lessons and playing on coached sports teams), and less time in self-directed and peer-negotiated activities (such as playing “pick up” sports games with other children) showed worse intrinsic executive functioning (Barker et al., 2014). The researchers concluded that the time these children spent in structured learning activities
limited their opportunities to learn to manage themselves in natural and informal learning contexts, which are critical for effective learning in the real world. Components of executive function develop and decline in neither linear nor binary (all or nothing) fashion. Both positive and negative age-related neurocognitive changes depend on the specific executive processes being engaged (Spreng et al., 2010; Turner and Spreng, 2012). Across many domains, older adults often achieve good performance by recruiting different processes than those engaged by younger adults.
The capacity to understand and direct one’s own learning is important not only in school but also throughout life. When learners are self-regulated, they have more control over the strategies and behaviors they use to learn. Self-regulation allows them to more effectively direct their cognitive activity by voluntarily setting learning goals, identifying methods for achieving them, actively pursuing those methods, and tracking progress toward the goals. Regulating one’s learning requires monitoring of activities, thoughts, and emo-
tions and making the adjustments necessary to achieve goals (Loyens et al., 2008). It also is facilitated when the expectations of educators accommodate learners’ interests and developmentally appropriate work, so that learners take responsibility for their goals and perceive that they have the power to make important decisions related to their mode of learning (Patall, 2013).
Self-regulation is a key element of the broader concept of metacognition, the capacity to reflect on and monitor one’s own cognitive processes. Monitoring and regulating cognition are sets of interrelated processes. Monitoring processes are those involved in assessing one’s own cognitive activities, including learning and memory. The processes of regulation allow the individual to control the decision processes and actions in ways directed by his monitoring (Bjork et al., 2013; Dunlosky and Metcalfe, 2009).
The growing body of research in this area has highlighted how difficult it is for people to regulate their own learning in formal educational settings and the corresponding value of training to improve this capacity. The complex processes involved have been the subject of a considerable amount of theoretical and experimental work in the past decade (Vohs and Bauminster  is a comprehensive handbook of recent research). A number of models have been proposed to characterize self-regulation processes, which suggest directions for interventions to improve learners’ capacity to direct their learning (Panadero, 2017). For example, Hattie and Donoghue (2016) identified more than 400 strategies found in the research literature on learning strategies. This body of work has explored the basic regulatory processes and the influence of emotion, desire, and habits; the role of personality traits; the physiological processes involved in self-regulation and how they develop; and many other issues. (Ways that educators can foster self-regulation in their students are discussed in Chapter 7.)
Growing understanding of the variety of variables that contribute to an individual’s capacity to regulate her learning complicates the task of succinctly defining what is involved. Nevertheless, the concept is generally understood to encompass personal characteristics, learning contexts, and motivational and regulatory processes, and all of these factors influence learning outcomes. Self-regulation is both a self-directive process and a set of thought patterns through which learners organize their activities to build skills. Successful self-regulated learners have developed the skills and habits to be effective learners, exhibiting effective learning strategies, effort, and persistence.
In one formulation, self-regulation is described as the interplay of the will to invest in learning, curiosity and a willingness to explore what one does not know, and the skills to pursue a deeper understanding of content (Hattie and Donoghue, 2016). Put another way, it is the “self-corrective adjustments [that] are taking place as needed [for the learner] to stay on track, whatever [the learner’s] purpose is” (Carver and Scheier, 2017, p. 3). This capacity is driven from within, by intrinsic goals and responses to experience. Many
factors influence self-regulation, ranging from sleep to personality traits to social and cultural influences and beyond. Research is ongoing in this field and continues to enlarge the picture of the importance and complexity of self-regulation for learning.
HPL I summarized research by neuroscientists and cognitive scientists on memory processes (National Research Council, 2000). This work had shown that memory is not a unitary construct that occurs in a single area of the brain. Instead, it comprises distinct types of processes associated with different memory functions. Not only are the processes of memory complex in themselves; but they also interact with other learning processes, such as the capacity to generalize (e.g., discrimination, categorization) and reason (e.g., comprehension, sense-making, causal inference).
A metaphor people commonly use to think about encoding and retrieving memories is that of spatial storage and search (Roediger, 1980). In this metaphor, the mind is imagined as a physical space and bits of knowledge (memories) are imagined as objects stored in that space. For instance, the knowledge might be pictured as a collection of books stored on shelves in a library, files stored in cabinets, or digital files stored on a computer hard drive. Accordingly, learning is imagined as a process of creating and storing new files containing different sorts of knowledge, with the hope that those files can be found when needed.
This mental file cabinet view of the mind and memory is compelling, but researchers have rejected the idea that knowledge (memories) consists of copies of experiences stored in one’s mind. Instead, learning and memory systems give people the ability to produce knowledge without storing copies of it. Many other systems of the body work in a similar way. For instance, the visual system gives us the ability to perceive objects in the world, but copies of those objects are not stored in the eye. Sensory systems give us the ability to experience a wide variety of sensations without storing them in the body. Consider what happens if you were to pinch your arm and experience pain. It would be strange to say that when your arm was pinched, the pain was “retrieved” from some place where it was “stored” in your arm. Instead, sensory systems provide the appropriate architecture to convey information to the brain, which then constructs the sensory stimulation into an experience.
What the storage metaphor does not capture is the fact that learning actually involves skills for reconstructing memories based on past experiences and cues in the present environment, rather than reproducing copies of an
experience. Reconstruction is made possible by the way memories are encoded and stored throughout the brain. Each individual processes memories from a subjective perspective, so that his own memories of the same information or episode will not be identical to those of another person. An important point is that reconstruction of some kinds of knowledge is so implicit and automatic that it feels fluent rather than rebuilt: for a skilled reader and writer, for example, it is not necessary to continually, consciously reconstruct memories of grammar (see Chapter 3 for discussion of types of learning).
When an individual constructs an experience, a representation of that experience is left behind in the brain that she may be able to draw upon in the future. The representation is not a perfect copy of the world but rather a partial record of the individual’s subjective interpretation and perception, which is in turn shaped by prior knowledge, experiences, perceptual capabilities, and brain processes. The processes involved in transforming “what happens” into mental representations are known as encoding. Over time and with sleep, an encoded memory may be consolidated, a process whereby the neural connections associated with it are strengthened and the memory, or representation of the experience, is stabilized, or stored. Retrieval refers to the processes involved in reconstructing memories of past experiences. Retrieval processes are triggered and guided by retrieval cues in the learner’s environment (e.g., prompts, questions, or problems to be solved) or in the learner’s mind (other thoughts or ideas that have some relationship to the memory).
For example, in practicing the guitar, a student’s eyes pass over the spots of ink on the sheet music and visual inputs register in the primary visual areas at the back of the brain, creating the visual part of the pattern of the music. At the same time, the sounds the student creates as he strums the guitar contribute to the pattern by registering in his auditory areas, some of which are consonant with the spots on the page and others less so. Somatosensory areas also contribute to the pattern by registering the position of the fingers on the neck of the guitar as the student plays. Although the inputs from each of the sense modalities register in different areas of the brain (together called the information-processing system), they are pulled together in what are called association areas, contributing to the unified experience of “playing music.” At the same time, the association and sensory-motor brain areas contain traces of patterns remaining from previous experiences of playing the guitar and other activities and knowledge, and these are retroactivated, allowing the current guitar-playing experience to be enriched by the person’s prior learning and expectations. For long-term skill development and learning to occur, the distributed pattern of inputs contributing to the current experience (visual, motor, auditory, emotional, etc.) must be consolidated and integrated with stored memory representations from prior experiences. This is why deliberate practice is needed for long-term robust learning.
Because they are reconstructed, memories are not frozen in time; they are
reconstructed anew each time a person recalls something, and the reconstruction takes into account current knowledge, expectations, and context. For this reason, memories are not fixed but instead morph over time, and they may omit details or include fabricated details that did not occur. This is especially evident when people repeatedly remember the same event: what people report will change over time as new information and suggestions become incorporated into the rich, potentially multisensory tapestry of representations physically consolidated across the brain.
Reconstructive processes are at work even when a person remembers highly emotional and unique events, such as the attack in the United States on September 11, 2001 (9/11), as a study by Hirsch and colleagues demonstrates (Hirst et al., 2015). These researchers asked people to report on their memories of 9/11: the circumstances in which they learned about the event as well as details about the attack itself. People were surveyed about their memories at four intervals beginning approximately 1 week after the event and concluding approximately 10 years later. The researchers found that the study participants forgot many of the details they reported during the first year and that their reports, even of emotionally charged and distinctive “flashbulb memories,” changed as time passed.
However, it is not only complex knowledge and events that must be reconstructed through the processes of memory. Even a simple task such as remembering a short list of words for a short amount of time requires active reconstruction. For example, when people were asked in a 1995 study to listen to short lists of related words, such as bed, rest, tired, awake, dream, and snooze, and later recall as many of the words as they could, they were highly likely to recall related words that were not on the list, such as sleep (Roediger and McDermott, 1995). This study showed that rather than simply reproducing encoded copies of the words, the study participants actively attempted to reconstruct even an event as simple as encountering a short word list.
The fact that the processes involved in reconstructing knowledge are driven by cues is well established in the study of memory. As early as 1923, a researcher demonstrated differences in people’s capacity to recall the (then 48) U.S. states when asked to do so twice at a 30-minute interval: the only difference in the two tests was the retrieval context (Brown, 1923). The retrieval cues available in a learner’s environment are critical for what she will be able to recall, and changing the retrieval context and cueing environment changes what a person expresses at any given moment in time (Tulving and Thomson, 1973). Thus, if a person fails to remember a fact or skill at a particular time, that does not necessarily mean he does not possess the necessary knowledge.
The importance of retrieval cueing has been shown for complex as well as simple learning scenarios. In another classic study, Anderson and Pichert (1978) had students read a story about a series of events in a house and then recall details from the story from one of two perspectives: the perspective of
a burglar or the perspective of a person buying a home. When students shifted perspectives, they recalled new information that they had not recalled the first time. Only the retrieval conditions had changed. Students had encoded and stored the same story, but what they recalled depended on the cues to which they were attending. In a similar study, Gick and Holyoak (1980, 1983) showed that people’s ability to solve a problem differed significantly with changes in the retrieval environment—in this case the instructions they received about how to use the materials they were to draw on in solving the problem.
There are two related implications of this work for educators and others interested in assessing people’s learning. First, undue weight should not be placed on any single assessment of a learner’s knowledge and skills. Second, memories are reconstructed more easily in situations that feel conducive and relevant to the content of the memory. The way a learner will retrieve particular knowledge and skills varies with the cues that trigger the reconstruction; the cues, in turn, are partly dependent on the emotional, social, and cognitive state of the learner at that moment. For example, a student who prides herself on baseball skills may have no trouble calling up knowledge of statistics during a game but may draw a blank in a high-stakes math test. In part to circumvent this problem, some researchers have proposed the use of dynamic assessments that present learners with multiple assessments and that may allow some form of instruction or feedback between attempts (Koedinger et al., 2012). Another strategy is to help learners recognize and leverage their strengths in other contexts. For example, an educator might remind a baseball player to think about baseball when he has trouble remembering what he knows about statistics during a math test, or encourage a young child who helps with cooking at home to connect her understanding of the proportions of ingredients to call on this knowledge when learning about formal proportionality in math class.
Information may be rehearsed in the mind just for short periods of time, for use in a particular activity, or it may be retained long term so it can be retrieved together with other experiences far in the future. Long-term memory has obvious importance for learning, but short-term, or working, memory also plays a prominent role in complex cognitive tasks and daily activities, such as mental arithmetic (e.g., calculating a tip) and reading (Moscovitch, 1992).
In practice, working memory is associated with academic achievement, including both math and reading skills (e.g., Bull and Scerif, 2001; Nevo and Breznitz, 2011). Keeping information temporarily in mind and manipulating it is necessary for key learning tasks such as remembering lengthy instructions
or keeping track of a problem being solved, and low working-memory capacity puts children at risk for poor academic progress (Alloway and Gathercole, 2006; Alloway et al., 2009). Low working memory has also been associated with learning disabilities (e.g., Gathercole et al., 2006; Geary et al., 2012; Smith-Spark and Fisk, 2007; Wang and Gathercole, 2013) and such developmental disorders as attention deficit/hyperactivity disorder (e.g., Willcutt et al., 2005), specific language impairment (e.g., Briscoe and Rankin, 2009), and autism (e.g., Williams et al., 2006).
Working-memory performance declines beginning in middle age (Bopp and Verhaeghen, 2005; Park et al., 2002; Verhaeghen and Salthouse, 1997). The primary cause of this decline seems to be age-related difficulty in attentional control (Fabiani et al., 2006; Hasher et al., 2008). Individual differences in working-memory capacity are relatively stable over time, but recent studies suggest that intervention during childhood may have benefits for specific working-memory outcomes (Holmes et al., 2009; Thorell et al., 2009).
There are three types of long-term memory: procedural, episodic, and semantic. Procedural or implicit memory is unconscious, but the other two involve conscious awareness of past events as episodes in one’s individual history (e.g., episodic memory of meeting a friend for the first time) or facts and concepts not drawn from personal experience (e.g., semantic memory of state capitals). A complex operation such as learning to play the guitar involves the gradual and incremental processes of motor learning (using implicit memory) to improve finger work, as well as the episodic memory processes involved in trying to internalize and later repeat specific skills taught in a lesson, such as playing a particular chord sequence, semantic memory for information such as key signatures, and emotional memories of successfully playing beautiful music.
Although some memories may last a lifetime, all are reworked over time, and most fall victim to disruption and interference and are rapidly forgotten. If at some later time, the guitar student is reminded of a particular practice episode by a relevant cue or prompt and tries to recall it, he will not be able to recreate the entire episode in his memory or to play as he did before because some of the necessary representations and motor sequences will no doubt have been weakened or lost. Moreover, he will have experienced other, similar episodes of music and of playing the guitar; his memory of the practice episode may feature bits of information that were not actually part of that particular episode but are consistent with it.
The fact that new learning starts off as a distributed pattern of neural activation that must be stabilized and integrated with existing knowledge stores to be retained as long-term memory contributes to challenges for young learn-
ers. One reason is that the neural machinery they have available to register the experiences, stabilize and integrate them, and later retrieve the stored products, is relatively immature and therefore works less efficiently and effectively. Young learners (and beginners in a domain) also have fewer memories of previous experiences in similar situations to call upon, or retroactivate. Metaphorically, although the learning experience itself may be richly textured, by the time it is processed through an immature neural architecture, with a less well-developed set of cognitive, cultural, and social-emotional expectations or schema, it may lose many of its attributes and features, so that the representation of the experience (the memory) is impoverished. An adult’s more mature neural structures and networks manage to retain many more of the features of the original experience. For this reason, for many domains of formal learning, young learners generally require more support, relative to older learners. At the same time, young learners may be exquisitely sensitive to certain kinds of learning, such as what they learn from parents’ emotional reactions to their behaviors.
Cultural differences in long-term memory capacities have been observed, such as in several studies that compared the capacity for detailed recall of specific events among European Americans, Asians, and Asian Americans (Han et al., 1998; Mullen, 1994; Wang, 2004; Wang and Conway, 2004; for a review, see Wang and Ross, 2007, but also see Ji et al., 2009, for the opposite pattern in an academic context). These researchers have identified differences in recall in preschoolers through adults and have suggested several hypotheses to explain them. Among the hypotheses are that cultural traditions and differences, such as in the way adults talk with preschoolers about personal experiences, may lead learners to attend to different aspects of events they experience (e.g., Leichtman et al., 2000; Wang, 2009) or to tend to use personal memories differently—for example, to guide decisions or to learn moral lessons and norms (e.g., Alea and Wang, 2015; Alea et al., 2015; Basso, 1996; Kulkofsky et al., 2009; Maki et al., 2015; Nile and Van Bergen, 2015; Wang and Conway, 2004).
This research has not definitively established the existence of or basis for cultural differences, and we note the risk of overgeneralizing between-group differences. However, it does suggest that the nature and form of memory for episodes may be influenced by culture.
Memory for episodes of new learning is critically important because it allows for rapid, even one-trial, learning and retention of new information (e.g., Bauer and Varga, 2015). It is one of the building blocks for cognitive growth during development and throughout the life span. One of the most significant changes learners experience in the first two decades of life is an increase
in the amount of information they remember. As young learners develop, their memories also become more deliberate and strategic and they impose increased organization on the material they are learning (e.g., Bjorklund et al., 2009). The organizations they use to conceptualize material and to focus on different features and processes depend on their development and their environment and are therefore deeply cultural and situated. Children become increasingly aware of their own and others’ memory processes as they develop (i.e., their metamemory improves), which enables them to recruit information-processing resources to assist with increased memory demands (see Box 4-2).
Though many memories of distinct learning episodes persist even into old age, people tend to have increased difficulty in forming memories of new episodes as they age. Normal aging is accompanied by a gradual decline in episodic memory that begins as early as the twenties and accelerates precipitously after the age of 60 (Salthouse, 2009). This decline is associated with degradation in a key aspect of episodic memory: the ability to anchor or bind an event to one’s personal past and to a location (e.g., Fandakova et al., 2014; Wheeler et al., 1997). This deficit can be manifested in a number of ways.
Older adults are more likely than younger adults to forget where or when an event occurred or to erroneously combine elements from different events (Spencer and Raz, 1995). Older adults may also be more likely than younger adults to bind irrelevant details (Campbell et al., 2010).
As people grow older, changes in memory consolidation and retrieval processes also may affect learning. Aging affects the ability to integrate information together as a memory is consolidated. These deficits can emerge even while information is still being held within working memory, which suggests that the deficits may at least in part reflect a lowered ability to maintain and encode the features of an experience into consolidated representations (e.g., Mitchell et al., 2000; Peich et al., 2013; van Geldorp et al., 2015). The finding that deficits in older adults’ binding can be reduced when they are given strategies that enhance memory consolidation supports this idea (e.g., Craik and Rose, 2012; Naveh-Benjamin and Kilb, 2012; Naveh-Benjamin et al., 2007; Old and Naveh-Benjamin, 2012). Another possible explanation is that older adults have a bias toward pattern completion: the process by which a partial or degraded memory cue triggers an individual to use other prior knowledge and experiences to reconstitute a complete memory representation (Stark et al., 2010).
Binding and pattern completion are likely to be part of the explanation for why older adults are more likely than younger adults to retain the “gist” of an event but not its specific details. For instance, after reading a list of associated words, older adults will be less likely than younger adults to remember each individual word presented on the list, but they will be at least as likely as younger adults to remember the themes of the list or to falsely remember nonpresented words that are thematically associated with the presented words (reviewed by Schacter et al., 1997). Similarly, older adults are more likely to remember the moral of a story rather than its details (Adams et al., 1990) and to report general rather than specific details of past autobiographical events (e.g., Schacter et al., 2013). Studies show that declines in the specificity of memory likely begin in middle age, with increases in gist-based false memory already apparent by the time an adult is in her 50s (Alexander et al., 2015).
Although these age differences are often framed as deficits, they do not always result in declines and can in fact be useful. The shift toward gist-based memory with age can lead older adults to be more likely than younger adults to remember the “big picture” or important implications (McGinnis et al., 2008). The shift toward pattern completion also may enable older adults to note connections among events and to integrate across experiences, abilities that often are considered part of the wisdom that is acquired with age (Baltes and Staudinger, 2000).
Executive function and self-regulation are critical processes for supporting learning. Both involve sets of processes that are related to success in school. Self-regulation involves many complex components, and researchers are actively working to understand how these components interact and how to support their development.
Memory is an important foundation for most types of learning. People’s learning and memory systems give them the ability to use past experiences to adapt and solve problems in the present. This ability to use the past by retrieving memories when they are needed is reconstructive in nature. It is not a process of searching for stored copies of mental representations of information and experiences but a set of processes triggered by cues in the learner’s environment through which he reconstructs these experiences and forges new connections for them. The retrieval cues available in a learner’s environment are critical for what she will be able to recall and also play a role in the way the learner begins to integrate new information as knowledge.
CONCLUSION 4-1: Successful learning requires coordination of multiple cognitive processes that involve different networks in the brain. In order to coordinate these processes, an individual needs to be able to monitor and regulate his own learning. The ability to monitor and regulate learning changes over the life span and can be improved through interventions.
CONCLUSION 4-2: Memory is an important foundation for most types of learning. Memory involves reconstruction rather than retrieval of exact copies of encoded mental representations. The cues available in a learner’s environment are critical for what she will be able to recall; they also play a role in the way the learner begins to integrate new information as knowledge.