What Can Be Learned About a Culture From Reading and Attending to Factual Materials

Suggested Citation:"5 Noesis and Reasoning." National Academies of Sciences, Technology, and Medicine. 2018. How People Learn 2: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

5
Knowledge and Reasoning

This chapter examines the development of cognition equally a primary outcome of learning and how learning is affected by accumulating knowledge and expertise. HPL Iⁱ emphasized these topics as well, but subsequent research has refined and extended understandings in a variety of learning domains. The beginning department of this chapter describes the trouble of knowledge integration from the perspective of learning scientists and illustrates with research findings how people integrate their knowledge at unlike points in their development and in different learning situations. The 2d section describes what is known about the effects of accumulated knowledge and expertise on learning. The second half of the affiliate discusses strategies for supporting learning. The commission has fatigued on both laboratory- and classroom-based research for this chapter.

HPL I noted that the mind works actively to both store and recall information past imposing structure on new perceptions and experiences (National Research Council, 2000). A central focus of HPL I was how experts structure their knowledge of a domain in ways that allow them to readily categorize new information and determine its relevance to what they already know. Considering novices lack these frameworks, they accept more than difficulty assimilating and later recalling new data they encounter. This chapter expands on these themes from HPL I, citing relevant inquiry reported since that study.

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¹ As noted in Chapter one, this report uses the abridgement "HPL I" for How People Acquire: Brain, Mind, Experience, and Schoolhouse: Expanded Edition (National Research Quango, 2000).

Suggested Commendation:"5 Knowledge and Reasoning." National Academies of Sciences, Applied science, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

Edifice A KNOWLEDGE Base of operations

Knowledge integration is a procedure through which learners put together different sorts of information and experiences, identifying and establishing relationships and expanding frameworks for connecting them. Learners must not only accumulate knowledge from individual episodes of experience simply likewise integrate the noesis they proceeds beyond time, location, circumstances, and the various formats in which knowledge appears (Esposito and Bauer, 2017). How knowledge acquired in detached episodes is integrated has been debated for decades (Karmiloff-Smith, 1986, 1990; Mandler, 1988; Nelson, 1974). Some researchers take suggested that infants are born with foundational knowledge that provides the elements necessary for learning and reasoning about their experiences (Spelke, 2004; Spelke and Kinzler, 2007) or that infants tin build from basic inborn reflexes to actively appoint with the world and gradually build skills and knowledge (Fischer and Bidell, 2006). Others accept argued that all noesis is generated through an individual's direct experience with the world (Greeno et al., 1996; Packer, 1985).

More recent work suggests that the integration of knowledge is a natural byproduct of the germination and consolidation of episodic memories (Bauer, 2009; Bauer et al., 2012). As described in Affiliate iv, when a memory is consolidated, the learner associates representations of the elements of the experience (e.k., sights, sounds, tactile sensations) and these associations serve to help stabilize that memory. At the same time, these representations may also be linked with older memories from previous experiences that accept already been stored in long-term retention (Zola and Squire, 2000). The fact that old and new retention traces can exist integrated shows that these traces are not stock-still. Instead, elements common to the new and stored memory traces reactivate the quondam memory and, every bit the new memory is consolidated, the old memory may be reconstructed and undergo consolidation once again (Nader, 2003). When information from either learning episode is later retrieved, elements of both memory traces volition be reactivated and will be simultaneously available for reintegration. As memory traces with common elements are simultaneously activated and linked, knowledge is expanded and memories are iteratively reworked. Figure 5-ane illustrates how this happens.

These linked traces may and so be integrated with additional new information that comes to the learner later, and another new retention trace undergoes consolidation. Interestingly, it is exactly this process of integration of information from different episodes that may explain why people are sometimes unable to explain when and where they gained particular noesis. Because the data generated by memory integration was not really experienced equally a single event, the data was non tagged with its origin (Bauer and Jackson, 2015).

The studies of knowledge acquisition in children and college students presented in Box 5-1 illustrate the capacity to integrate unconnected infor-

Suggested Commendation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

**Effigy 5-1** *Depiction of retention integration.*
Notation: Imagine that while walking in the park you see a adult female walking her dog (initial experience). This experience is connected to a group of neurons that are activated simultaneously past it (this neural representation is represented by the blue disc). Later, you come across the same dog while walking in the city, simply this time a man is walking the dog (perceptually overlapping event). The dog (common element) triggers reactivation of your initial feel in the park. This reactivation allows connections of neural representations to exist formed regarding the dog, the woman, and the man, such that the events are linked across time. This integration of memories allows you to create new knowledge. That is, you accept learned that there is a relationship betwixt the man and the woman, though you have non seen the man and the woman together.
*SOURCE: Adapted from Schlichting and Preston (2015, Fig. ane).*

mation and retain this knowledge starting at a very young age. These studies underscore the active role of the learner; that is, fifty-fifty young children exercise not simply accrue knowledge from what they take experienced directly but build knowledge from the many things that they accept figured out on their own, which, over fourth dimension, they tin do with less repetition and external support.

As discussed in Chapter ii, acceptable sleep is important for integration and learning. The encephalon continues the work of encoding and consolidation during slumber and facilitates generalizations across learning episodes (Coutanche et al., 2013; Van Kesteren et al., 2010). Specifically, activation of the hippocampus (which plays a key part in memory integration) during sleep seems to permit connections betwixt memory traces to be formed across the cortex. This process promotes the integration of new information into existing memory traces, allows for abstraction across episodes (Lewis and Durant, 2011), and leads to the possibility of building novel connections, which may be both creative and insightful or may be bizarre (Diekelmann and Born, 2010).

Suggested Citation:"5 Noesis and Reasoning." National Academies of Sciences, Technology, and Medicine. 2018. How People Larn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: ten.17226/24783.

Suggested Citation:"five Knowledge and Reasoning." National Academies of Sciences, Applied science, and Medicine. 2018. How People Larn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

Suggested Commendation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering science, and Medicine. 2018. How People Acquire II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: x.17226/24783.

KNOWLEDGE AND EXPERTISE

When people repeatedly engage with like situations or topics, they develop mental representations that connect disparate facts and deportment into more effective mental structures for acting in the world. For example, when people first move to a new neighborhood, they may larn a set of detached routes for traveling between pairwise locations, such as from home to school and from home to the grocery store. Over fourth dimension, people naturally develop a mental representation of spatial relationships, or mental map, that stitches these discrete routes together. Even if they have never traveled between the school and the grocery shop, they can figure out the most efficient route by consulting their mental map (Thorndyke and Hayes-Roth, 1982). The observation that experts in a domain have developed frameworks of information and agreement through long experiences in a particular area was a fundamental focus of HPL I. In this department, we briefly depict some of the benefits of proficient knowledge (a more detailed word of the benefits of expertise appears in HPL I) and so discuss the knowledge-related biases that may come with expertise.

Benefits of Expertise

One of the almost well-documented benefits of the acquisition of knowledge is an increase in the speed and accurateness with which people tin can complete recurrent tasks: remembering a solution is faster than trouble solving. Another do good is that people who develop expertise tin handle increasingly circuitous bug. One way this occurs is that people main substeps, and so that each substep becomes a chunk of knowledge that does non require attending (eastward.g., Gobet et al., 2001). People likewise learn to handle complication by developing mental representations that make specific tasks easier to complete. When Hatano and Osawa (1983) studied abacus masters, they institute that fifty-fifty without an abacus in front end of them, the masters had biggy memories for numbers and could carry out addition issues with very large numbers because they had developed a mental representation of an abacus, which they manipulated virtually. These abacus masters did not bear witness similarly superior ability to remember or keep rails of messages or fruits—tasks that were not aided by manipulating a virtual abacus.

A third benefit is an increase in the ability to extract relevant information from the environment. Experts not only have amend-developed noesis representations than novices have but also can perceive more than information that is relevant to those representations. For example, radiologists are able to see telling patterns in an ten-ray that announced just as shadows to a novice (Myles-Worsley et al., 1988). The ability to discern more precise information complements a more-differentiated mental representation of those phenomena.

Suggested Citation:"v Cognition and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Acquire II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Printing. doi: 10.17226/24783.

An implication of this ability is that students need to learn to encounter the relevant information in the environment to assist differentiate concepts, such every bit the difference between a positive and a negative curvilinear gradient (Kellman et al., 2010).

A fourth do good of acquiring expert cognition is that information technology helps people employ their environment equally a resource. Using what is known as distributed cognition, people can offload some of the cognitive demands of a task onto their surround or other people (Hollan et al., 2000). For instance, a major goal of learning is to develop knowledge of where to look for resource and assistance, and this is all the same of import in the digital age. Experts typically know which tools are available and who in their network has specialized expertise they can call upon.

Finally, acquiring knowledge helps people proceeds more knowledge by making it easier to learn new and related data. Although some cerebral abilities related to learning novel information refuse, on average, with age, these declines are outset by increases in knowledge accumulated through the life span, which empowers new learning. For case, in a written report of young adults and older adults (in their 70s) who listened to a broadcast of a baseball game, the older adults who knew a lot about baseball game recalled more of the circulate than the young adults who knew less virtually baseball. This occurred despite the fact that the younger adults had superior executive functioning (Hambrick and Engle, 2002).

Bias as a Natural Side Effect of Knowledge

As people'southward knowledge develops, their thinking likewise becomes biased. Simply the biases may be either useful or detrimental to learning. The word "bias" often has negative connotations, but bias as understood past psychologists is a natural side result of knowledge acquisition. Learning biases are often implicit and unknown to the individuals who concur them. They appear relatively early in knowledge conquering, as people begin to form schemas (conceptual frameworks) for how the world operates and their identify within information technology. These schemas help individuals know what to wait and what to attend to in detail situations (east.g., in a doctor's office versus at a friend's political party) and assistance them develop a sense of cultural fluency—that is, to know how things work "around here" (Mourey et al., 2015).

Psychologists distinguish two types of bias: one is intrinsic to learning and primarily useful and empowering to the learner; the 2nd occurs when prior experiences or beliefs undermine the acquisition of new knowledge and skills.

An aphorism from the context of medical diagnosis illustrates the two types of bias: "When you hear hoof-beats, think of horses not zebras." In the United States, horses are much more common than zebras so one is much more likely to encounter the common "horses" than the rare "zebras." Of course, one should change assumptions in light of additional bear witness: if the

Suggested Citation:"v Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

large mammal from which the hoof-beats emanate has blackness and white stripes, information technology is much more than likely to be a zebra than a horse. Thus, if one sees a striped animal in a zoo but insists that it is a equus caballus and not a zebra, this resistance to new data is a stiff form of the limiting effects of bias on learning. A person may fail even to observe the zebra at the zoo because he was so strongly expecting to see a horse instead and was attuned to notice only that kind of animal.

Making matters fifty-fifty more complicated, two people who have different prior levels of expertise, or unlike beliefs, might legitimately accept different interpretations when initially presented with the same information. But if sufficient additional information suggests a particular interpretation, they should converge on an answer, especially if the higher level of expertise is brought to conduct.

Beliefs about human-acquired global climate change are a good case of the biases that blind individuals to new evidence. Despite nigh universal consensus among climate scientists that global climatic change is taking place and that this change is induced by humans' behavior, a considerable proportion of adults in the Usa practise non have these interpretations of the bear witness. One might expect that higher levels of science literacy would be associated with greater understanding with the scientific consensus. However, Kahan and colleagues (2012) institute that it is amidst the individuals with the highest levels of science literacy that the virtually stark polarization is apparent. Those who only seek out and attend to information consistent with their prior beliefs will create an "echo-sleeping accommodation" that further biases their learning. Oftentimes this echo-sleeping accommodation result is socially reinforced, as individuals adopt to talk over the topic in question with others whom they know agree beliefs like to their own.

Stereotypes perpetuate themselves through learned bias, but not all learning biases are considered to have negative consequences. For example, some positive biases promote well-existence and mental wellness (Taylor and Chocolate-brown, 1988), some may promote accuracy in perceptions of other people (Funder, 1995), and others may be adaptive behaviors—for example, selective attention and action in situations in which errors have a high cost (Haselton and Kiss, 2000; Haselton and Funder, 2006). Hahn and Harris (2014) have written a useful historical overview of research on bias in human cognition.

Nonetheless other biases refine perception and serve to blur distinctions inside categories that are not meaningful while highlighting subtle cross-category distinctions that may be important. For example, very young infants respond equally to phonological contrasts that matter in their language (e.g., "r" and "50" if the infant lives in an English-speaking context) and those that do non affair (e.g., "r" and "l" in a Japanese-speaking context). Over time, infants lose this discriminatory adequacy. This loss is really a benefit, reflecting the babe'southward increasing efficiency in processing his own linguistic communication context, and is a marker of

Suggested Citation:"v Knowledge and Reasoning." National Academies of Sciences, Applied science, and Medicine. 2018. How People Learn 2: Learners, Contexts, and Cultures. Washington, DC: The National Academies Printing. doi: 10.17226/24783.

learning (Kuhl et al., 1992). In the other management, dermatologists may learn from experience and formal grooming to distinguish subtle features of moles and peel growths that signal malignancy, features that to an untrained eye are indistinguishable from those of beneficial growths.

Biases touch the noncognitive aspects of learning as well. In a variable earth, highly stable task environments are not guaranteed so training to high efficiency may actually create a mindset that makes new learning more than difficult, impeding motivation and interest in continuous growth and development. For instance, a person who has learned how to organize her schedule using a specific tool may be reluctant to learn a new tool because of the perception that it will take too much time to learn to use information technology, fifty-fifty though information technology may exist more efficient in the long run. In this example, it is non that the person is unable to acquire the new tool; rather, her beliefs about the corporeality of endeavor required affect her motivation and interest in learning. This kind of self-attribution, or prior cognition of oneself, can have a large influence on how people arroyo hereafter learning opportunities, which in plow influences what they will learn (Blackwell et al., 2007).

KNOWLEDGE INTEGRATION AND REASONING

Nosotros take seen that edifice a knowledge base requires doing iii things: accumulating information (in function by noticing what matters in a situation and is therefore worth attention to); tagging this information as relevant or not; and integrating it across split episodes. These three activities can happen relatively chop-chop and automatically, or they can happen slowly through deliberate reflection. However, these processes alone are not sufficient for integrating and extending cognition. Learners of all ages know many things that were not explicitly taught or directly experienced. They routinely generate their own novel agreement of the information they are accumulating and productively extend their knowledge.

Inferential Reasoning

Inferential reasoning refers to making logical connections between pieces of information in gild to organize knowledge for understanding and to drawing conclusions through deductive reasoning, inductive reasoning, and abductive reasoning (Seel, 2012). Inferential thinking is needed for such processes every bit generalizing, categorizing, and comprehending. The act of reading a text is a good example. To encompass a text, readers are required to make inferences regarding information that is only implied in the text (see, e.chiliad., Cain and Oakhill, 1999; Graesser et al., 1994; Paris and Upton, 1976). Some types of inferences aid readers track the meaning of a text past integrating unlike information information technology supplies, for example by recognizing anaphoric

Suggested Commendation:"5 Cognition and Reasoning." National Academies of Sciences, Engineering science, and Medicine. 2018. How People Acquire 2: Learners, Contexts, and Cultures. Washington, DC: The National Academies Printing. doi: 10.17226/24783.

references (words in a text that require the reader to refer back to other ideas in the text for their meaning). Other types of inferences let a reader to fill in gaps in the text by recruiting information from beyond it (i.e., groundwork knowledge), in order to empathize information within the text. Though these types of inferences are essential for agreement, they are thought to survive in working retentivity but long enough to aid comprehension (McKoon and Ratcliff, 1992).

Other inferences that learners make survive beyond the premises of working memory and become incorporated into their knowledge base. For example, a person who knows both that liquids aggrandize with estrus and that thermometers contain liquid may integrate these two pieces of information and infer that thermometers work because liquid expands equally oestrus increases. In this way, the learner generates understanding through a productive extension of prior learning episodes.

Constructive problem solving typically requires retrieved knowledge to be adjusted and transformed to fit new situations; therefore, memory retrieval must be coordinated with other cognitive processes. One way to assist people realize that something they have learned before is relevant to their current job is to explicitly requite them a hint that information technology is relevant (Gick and Holyoak, 1980). For example, such hints might exist embedded in text, provided past a instructor, or incorporated into virtual learning platforms. Another strategy for helping people realize that they already know something useful is to inquire people to compare related issues in order to highlight exactly what they accept in common, increasing the likelihood that they volition recall previously acquired knowledge with similar properties (Alfieri et al., 2013; Gentner et al., 2009).

Kolodner et al. (2003) gives the instance of an builder trying to build an office edifice with a naturally lit atrium. She realizes that a familiar library's design, which includes an outside wall of glass, could be reused for the part building, but would fit the building'due south needs amend if translucent drinking glass bricks were used instead of a clear, glass pane. This kind of blueprint-based reasoning is incorporated into problem-based learning (Hmelo-Silvery, 2004) activities. Problem-based learning emphasizes that memories are not simply stored to allow hereafter reminiscing, just are formed so that they can be used, reshaped, and flexibly adjusted to serve broad reasoning needs. The goal of problem-based learning is to instill in learners flexible cognition use, constructive problem-solving skills, self-directed learning, collaboration, and intrinsic motivation. These goals are in line with several of the goals identified in other contexts as important for success in life and work (National Research Council, 2012b).

Age-Related Changes in Knowledge and Reasoning

People's learning benefits from a steady increase, over many decades, in the aggregating of world knowledge (e.thousand., Craik and Salthouse, 2008;

Suggested Citation:"v Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Larn Ii: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

Hedden and Gabrieli, 2004). This aggregating makes it easier for older adults non merely to retrieve vocabulary and facts about the world (Cavanagh and Blanchard-Fields, 2002) but besides to acquire new data in domains related to their expertise. For case, physicians acquire medical expertise, which enables them to encompass and remember more information from medical texts than novices can (Patel et al., 1986). It is as well thought that older adults can compensate for declines in some abilities by using their all-encompassing earth knowledge. For case, medical experts depend less on working retentivity because they can depict on their expertise to reconstruct only those facts from long-term memory that are relevant to a current need (eastward.g., Patel and Groen, 1991).

The noesis learners accumulate throughout the life span is the growing product of the processes of both learning new information from direct feel and generating new data based on reasoning and imagining (Salthouse, 2010). These 2 cognitive assets together—accumulated knowledge and reasoning ability—are peculiarly relevant to healthy aging. Reasoning and cognition abilities tend to exist correlated. That is, people who have comparatively higher reasoning chapters are likely to acquire correspondingly more knowledge over the life span than their peers (Ackerman and Beier, 2006; Beier and Ackerman, 2005). Reasoning ability is a major determinant of learning throughout life, and it is through reasoning, specially in contexts that allow people to pursue their interests, that people develop knowledge throughout their life span (Ackerman, 1996; Cattell, 1987).

On boilerplate, however, the trajectories of reasoning and noesis acquisition are unlike across the life span. A number of research studies have described the general trajectories of historic period-related changes in power, using a multifariousness of measures and inquiry designs (cross-sectional and longitudinal), and have shown a fairly consistent tendency in which the evolution of knowledge remains steady as reasoning capacity (the power to quickly and accurately manipulate multiple distinct pieces of factual information to make inferences) drops off (Salthouse, 2010). Nevertheless, in that location is considerable individual variability in the trajectories, which reflect individual wellness and other characteristics, as well every bit educational and experiential opportunities and even social appointment. Yet, even though there is an average decline in inferential reasoning capacity through adulthood, at that place is non a respective turn down in the power to make good decisions—a more colloquial use of the word "reasoning." In other words, the research does not propose that the average xiv-year-old reasons better well-nigh what to do in a complex or emotional real-world situation than would an average 50-yr-old. Instead, it describes the 14-year-old's stronger ability to quickly manipulate multiple singled-out pieces of factual data to make logical and combinatorial inferences.

The growth or decline of abilities tin be expected to vary not merely between individuals only also within the same person over time (Hertzog et al.,

Suggested Commendation:"5 Cognition and Reasoning." National Academies of Sciences, Engineering science, and Medicine. 2018. How People Larn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: ten.17226/24783.

2008). Two 50-year-olds may have extremely different cognitive profiles, such that one may mostly have the same ability profile as an average 30-year-old and the other may more than closely resemble an average 70-year-quondam. Within the same person, abilities will refuse or grow at varying rates as a function of that individual's continuing employ of some skills and intellectual development in detail domains; losses and declines are associated with decay of other skills. (Factors that influence cerebral aging are discussed in Affiliate 9.) As mentioned, new learning depends on both reasoning ability and knowledge acquisition (Ackerman and Beier, 2006; Beier and Ackerman, 2005). Even though reasoning abilities refuse with age, knowledge accumulated throughout the life bridge facilitates new learning, as long as the information to be learned is aligned with existing domain knowledge. When people select environments for didactics, work, and hobbies that capitalize on their already-established cognition and skills every bit they age, their selectivity allows them to capitalize on their repertoire of knowledge and expertise for learning new information (Baltes and Baltes, 1990).

Cognitive abilities change throughout the life span in a diverseness of ways that may touch on a person'south ability to learn new things (meet Hartshorne and Germine, 2015, for discussion). For example, as people historic period, learning may rely more on knowledge and less on reasoning and quick manipulation of factual data. However, examining peoples' cognitive abilities and learning becomes increasingly complex equally people develop past the age of formal teaching. One reason is that the ways in which people learn become increasingly idiosyncratic exterior of a standardized educational curriculum, and understanding this procedure requires assessing knowledge gained through a wide multifariousness of developed experiences that different individuals amass over a lifetime (Lubinski, 2000). The unique complexities of developed learning and development are discussed in Chapter 8.

Furnishings of Civilisation on Reasoning

Every bit described in Chapter two, learning is inherently cultural, given that a person's experiences in a culture affect biological processes that back up learning, perception, and noesis. In the surface area of reasoning, for instance, researchers take explored key differences in peoples' reasoning about three basic domains of life: physical events (naïve physics), biological events (naïve biology), and social or psychological events (naïve psychology) (see e.1000., Carey, 1985, 2009; Goswami, 2002; Hirschfeld and Gelman, 1994; Spelke and Kinzler, 2007; also see Ojalehto and Medin, 2015c, for a review). These distinctions are compelling in the sense that each reflects a set of intuitive principles and inferences. That is, each domain is defined by entities having the same kind of causal backdrop. These might exist marked, for case, by the style they move: concrete entities are set into motion by external forces,

Suggested Citation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

while biological entities may propel themselves. These domains are important for agreement cognition because researchers have suggested that whereas the perception of physical causality is universal, causal reasoning in the biological and psychological domains is culturally variable.

Two studies illustrate means to examine these bug. Morris and Peng (1994) presented 2 types of blithe displays to American and Chinese participants. One set of displays depicted physical interactions (of geometrical shapes), whereas the other set depicted social interactions (among fish). The participants' answers to questions nigh what they had seen suggested differences in attention to internal and external causes across the groups, only those differences depended on the domain (social or concrete). The authors concluded that attribution of causality in the social domain is susceptible to cultural influences just that causality in the physical domain is non.

Beller and colleagues (2009) asked German, Chinese, and Tongan participants to signal which entity they regarded as causally most relevant for statements such every bit "The fact that woods floats on h2o is basically due to . . . ". Ratings varied by the cultural groundwork of respondents and also by the phenomena participants were considering. In general, the German and Chinese participants, but non the Tongan participants, considered a carrier's capability for buoyancy just when the floater was a solid object, such as wood, but not when it was a fluid, such equally oil (Beller et al., 2009; see also Bender et al., 2017). This is an area of research that has barely been explored, but results to engagement propose that the perception of physical causality may in fact not be universal and may be learned in culturally mediated ways.

STRATEGIES TO SUPPORT LEARNING

People are naturally interested in strengthening their ability to acquire and retain noesis and in ways to improve learning performance. Researchers accept explored a variety of strategies to back up learning and memory. They take identified several principles for structuring practice and engaging with information to be learned to ameliorate retentiveness, to make sense of new information, and to develop new cognition.

Several scholars accept looked across the research on the effectiveness of specific strategies for supporting learning (Benassi et al., 2014; Dunlosky et al., 2013; Pashler et al., 2007). The authors of these three studies looked for strategies that (1) have been examined in several studies, using accurate educational materials in classroom settings; (2) show effects that tin can exist generalized across learner characteristics and types of materials; (3) promote learning that is long-lasting; and (iv) back up comprehension, noesis application, and problem solving in improver to recall of factual material. These three analyses identified 5 learning strategies as promising:

Suggested Commendation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Acquire Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

retrieval practice;
spaced practice;
interleaved and varied practise;
summarizing and cartoon; and
explanations: elaborative interrogation, cocky-caption, and teaching.

Strategies for Noesis Memory

The first three strategies are means of structuring practice that are specially useful for increasing knowledge retention.

Retrieval Practice

Some show shows that the human action of retrieval itself enhances learning and that when learners exercise retrieval during an initial learning activity, their ability to recollect and use knowledge again in the future is enhanced (Karpicke, 2016; Roediger and Karpicke, 2006b). The benefits of retrieval practice in full general have been shown to generalize beyond individual differences in learners, variations in materials, and different assessments of learning. For example, researchers have plant effects beyond learner characteristics in children (Lipko-Speed et al., 2014; Marsh et al., 2012). Studies take also suggested that retrieval practice tin exist a useful retentiveness remediation method among older adults (Balota et al., 2006; Meyer and Logan, 2013; also see Dunlosky et al., 2013, for a review of constructive learning techniques). However, almost of this research has addressed retrieval of relatively uncomplicated information (due east.g., vocabulary), rather than deep understanding.

Research has also demonstrated the effects of retrieval practise on recall of texts and other information related to schoolhouse subjects. For instance, Roediger and Karpicke (2006a) had students read cursory educational texts and practice recalling them. Students in 1 condition read the texts four times; students in a 2nd grouping read three times and recalled the texts once past writing down every bit much as they could recollect; and students in a third group read the material in one case and then recalled information technology during three retrieval practice periods. On a final test given 1 week afterward the initial learning session, students who practiced retrieval one time recalled more than of the material than students who only read the texts, and the students who repeatedly retrieved the textile performed the all-time. The results suggest that actively retrieving the material soon later studying it is more productive than spending the same amount of fourth dimension repeatedly reading.

Attempting retrieval simply failing has likewise been shown to promote learning. Failed retrievals provide feedback signals to learners, signaling that they may not know the information well and should adjust how they encode the material the next fourth dimension they written report information technology (Pyc and Rawson, 2010). The deed of failing to call back may thus heighten subsequent encoding (Kornell, 2014).

Suggested Commendation:"five Knowledge and Reasoning." National Academies of Sciences, Applied science, and Medicine. 2018. How People Learn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

Such studies suggest that self-testing can be an constructive fashion for students to practice retrieval. However, prove from surveys of students' learning strategies and from experiments in which learners are given control over when and how often they can test themselves suggests that students may not test themselves oft or finer enough (Karpicke et al., 2009; Kornell and Son, 2009). Many students practise non engage in self-testing at all, and when students do test themselves, they oftentimes do and then as a "knowledge check" to meet whether they tin or cannot remember what they are learning. While this is an of import use of self-testing, few learners self-test because they view the act of retrieval as part of the process of learning. Instead, they are likely to call up something once and then, believing they have learned it for the long term, drop the particular from further do.

Spaced Practice

Researchers who take compared spaced and massed practise accept shown that the style that learners schedule practice can have an impact on learning (Carpenter et al., 2012; Kang, 2016). Massed practice concentrates all of the practice sessions in a curt period of fourth dimension (such as cramming for a test), whereas spaced practice distributes learning events over longer periods of time. Results show greater effects for spacing than for massed practice across learning materials (e.g., vocabulary learning, grammatical rules, history facts, pictures, motor skills) (Carpenter et al., 2012; Dempster, 1996), stimulus formats (east.g., audiovisual, text) (Janiszewski et al., 2003), and for both intentional and incidental learning (Challis, 1993; Toppino et al., 2002). Studies have shown benefits of spaced practise for learners of ages 4 through 76 (Balota et al., 1989; Rea and Modigliani, 1987; Simone et al., 2012; Toppino, 1991). Cepeda and colleagues (2006) found that spaced practise led to greater call up than massed practise regardless of the size of the lag between practice and recall.

At that place are many possible reasons why spaced practice might be more than effective than massed practice. When an item, concept, or procedure is repeated afterward a spaced interval, learners have to fully engage in the mental operations they performed the first fourth dimension considering of forgetting that has occurred. Just when repetitions are immediate and massed together, learners do non fully appoint during repetitions. In the case of reading, one possible reason why massed re-readings do non promote learning is that when people reread immediately, they do non attend to the nearly informative and meaningful portions of the cloth during the second reading, equally illustrated past Dunlosky and Rawson (2005) in a study of self-paced reading.

A few researchers have attempted to identify the spacing intervals that promote the most retention—a "sweet spot" where spaced do confers benefits before too much forgetting has occurred (Cepeda et al., 2008; Pavlik and Anderson, 2008). For example, a study of vocabulary learning among 5th

Suggested Citation:"v Noesis and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn Ii: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

graders suggested that a ii-calendar week interval showed the best results (Sobel et al., 2011). Another classroom-based report of spacing effects focused on first-course children learning to associate letters and sounds during phonics instruction (Seabrook et al., 2005). The children who received spaced do during the 2-week period significantly outperformed the children who received a single massed practise session each 24-hour interval.

In general, the literature on spaced practice suggests that separating learning episodes by at least one day, rather than focusing the learning into a single session, maximizes long-term retention of the material. However, it is important to annotation that wider spacing is not necessarily e'er improve. The optimal distribution of learning sessions depends at least in office on how long the material needs to exist retained in memory (i.e., when the material will be recalled or tested). For example, if the learner will be tested 1 month or more after the concluding learning session, then the learning should exist distributed over weeks or months.

Interleaved and Variable Practice

The way information is presented can significantly affect both what is learned (Schyns et al., 1998) and how well it is learned (Goldstone, 1996). Variable learning mostly refers to practicing skills in different ways, while interleaving refers to mixing in different activities. Varying or interleaving unlike skills, activities, or issues inside a learning session—as opposed to focusing on one skill, action, or problem throughout (called blocked learning)—may amend promote learning. Both strategies may besides involve spaced practise, and both too present learners with a diversity of useful challenges, or "desirable difficulties." Researchers take identified potential benefits of variable and interleaved practise learning, but they accept also plant a few benefits for blocked practice.

Several studies take shown benefits for blocking, at to the lowest degree for category learning (Carpenter and Mueller, 2013; Goldstone, 1996; Higgins and Ross, 2011). Moreover, when given the option, a bulk of learners preferred to cake their written report (Carvalho et al., 2014; Tauber et al., 2013). Interleaving can boost learning of the structure of categories; that is, learning that some objects or ideas belong to the same category and others do non (Birnbaum et al., 2013; Carvalho and Goldstone, 2014a, 2014b; Kornell and Bjork; 2008). Other researchers have examined interleaved exercise in mathematical problem-solving domains (Rohrer, 2012; Rohrer et al., 2015).

Carvalho and Goldstone (2014a) constitute that the effectiveness of the presentation methods (interleaved or blocked) depended on whether the participant engaged in active or passive report. They besides found that interleaving concepts improved students' chapters to discriminate among different categories, while blocked exercise emphasized similarities within each category. These results

Suggested Citation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Larn 2: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

propose that interleaved written report improves learning of highly like categories (past facilitating betwixt-category comparisons), whereas blocked study improves learning of low-similarity categories (past facilitating within-category comparisons).

Interleaved report naturally includes delays betwixt learning blocks and thus easily allows for spaced practice, which has the potential benefits for long-term retentivity discussed above. Nevertheless, it may be beneficial because it helps learners to brand comparisons among categories, not because information technology allows time to elapse between learning blocks (Carvalho and Goldstone, 2014b). The mechanisms that underlie the benefits of either interleaved or blocked study (due east.k, possible effects on attentional processes) are ongoing topics of research. As with other strategies, the optimal way to present material—interleaved or blocked—and the mechanisms near heavily involved will likely depend on the nature of the study task.

Strategies for Understanding and Integration

The other ii strategies for which in that location is strong show—summarizing and cartoon and developing explanations—draw on inferential processes that research shows to be effective for organizing and integrating data for learning.

Summarizing and Cartoon

Summarizing and cartoon are two common strategies for elaborating on what has been learned. To summarize is to create a verbal clarification that distills the most important information from a prepare of materials. Similarly, when learners create drawings, they use graphic strategies to portray important concepts and relationships. In both activities, learners must take the material they are learning and transform information technology into a different representation. There are differences between them, simply both activities involve identifying important terms and concepts, organizing the information, and using prior knowledge to create verbal or pictorial representations.

Both summarization and drawing have been shown to benefit learning in school-age children (Gobert and Clement, 1999; Van Meter, 2001; Van Meter and Garner, 2005). Literature reviews by Dunlosky and colleagues (2013) and Fiorella and Mayer (2015a, 2015b) have identified factors that appear to contribute to the effectiveness of summarization and drawing activities.

A few studies have suggested that the quality of students' summaries and drawings is directly related to how much they learn from the activities and that learners do these activities more finer when they are trained and guided (Bednall and Kehoe, 2011; Dark-brown et al., 1983; Schmeck et al., 2014). For example, the effectiveness of drawing activities is enhanced when learners

Suggested Citation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering science, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

compare their drawings to author-generated pictures (Van Meter et al., 2006). Similarly, providing learners with a listing of relevant elements to exist included in drawings and partial drawings helps learners create more complete drawings and bolsters learning (Schwamborn et al., 2010).

A group of researchers compared summarization and drawing and suggested that their effectiveness depends on the nature of the learning materials. For instance, Leopold and Leutner (2012) asked high school students who were studying a scientific discipline text about water molecules, which contained descriptions of several spatial relations, to either draw diagrams, write a summary of the text, or to re-read the text (the control condition). Those who created drawings performed meliorate on a comprehension exam than those who re-read the texts. However, those who created written summaries performed worse than those who re-read. The authors concluded that the drawing was more constructive in this instance because the learning involved spatial relations.

Note-taking, either writing by hand or typing on a laptop, is a form of summarizing that has also been studied. For instance, Mueller and Oppenheimer (2014) plant that students who paw-wrote notes larn more those who typed notes using a laptop figurer. The researchers asked students to take notes in these ii means and then tested their recall of factual details, conceptual agreement, and power to synthesize and generalize the information. They found that students who typed took more than voluminous notes than those who wrote by hand, only the hand-writers had a stronger conceptual understanding of the cloth and were more successful in applying and integrating the material than the typers. The researchers suggested that because writing notes by hand is slower, students doing this cannot take notes verbatim but must listen, digest, and summarize the material, capturing the chief points. Students who type notes can do so chop-chop and without processing the data.

Mueller and Oppenheimer (2014) also examined the contents of notes taken past higher students in these two ways across a number of disciplines. They establish that the typed notes—which were closer to verbatim transcriptions—were associated with lower retention of the lecture material. Even when study participants using laptops were instructed to recollect nigh the information and blazon the notes in their ain words, they were no better at synthesizing material than students who were not given the alarm. The authors concluded that typing notes does non promote understanding or application of the information; they suggested that notes in the students' ain words and handwriting may serve equally more effective memory prompts past recreating context (e.1000., thought processes, conclusions) and content from the original lecture.

Suggested Citation:"5 Knowledge and Reasoning." National Academies of Sciences, Applied science, and Medicine. 2018. How People Larn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: x.17226/24783.

Developing Explanations

Encouraging learners to create explanations of what they are learning is a promising method of supporting understanding. Iii techniques for doing this have been studied: elaborative interrogation, cocky-explanation, and educational activity.

Elaborative interrogation is a strategy in which learners are asked, or are prompted to enquire themselves, questions that invite deep reasoning, such equally why, how, what-if, and what-if not (as opposed to shallow questions such every bit who, what, when, and where) (Gholson et al., 2009). A curious student who applies intelligent elaborative interrogation asks deep-reasoning questions as she strives to comprehend difficult fabric and solve issues. Nevertheless, elaborative interrogation does not come naturally to most children and adults; grooming people to use this skill—and particularly training in asking deep questions—has been shown to have a positive impact on comprehension, learning, and memory (Gholson et al., 2009; Graesser and Lehman, 2012; Graesser and Olde, 2003; Rosenshine et al., 1996). For example, in an early report, people were asked either to provide "why" explanations for several unrelated sentences or to read and study the sentences. Both groups were then tested on their memory of the sentences. Those who asked questions performed amend than the group that only studied the sentences (Pressley et al., 1987). Studies with children have also shown benefits of elaborative interrogation (Woloshyn et al., 1994), and the benefits of elaborative interrogation tin persist over time (east.g., 1 or two weeks later learning), though few studies have examined effects of elaborative interrogation on long-term retention.

Most studies conducted by researchers in experimental psychology take used isolated facts as materials in studying the effects of elaboration and have assessed verbatim retention, but researchers in educational psychology have also looked at more complex text content and assessed inference making (Dornisch and Sperling, 2006; Ozgungor and Guthrie, 2004). For example, McDaniel and Donnelly (1996) asked college students to report short descriptions of physics concepts, such equally the conservation of angular momentum, and then answer a why question about the concept (east.g., "Why does an object speed up every bit its radius get smaller, equally in conservation of angular momentum?"). A terminal assessment involved both factual questions and inference questions that tapped into deeper levels of comprehension. The authors constitute benefits of elaborative interrogation for complex materials and assessments and also establish that those who engaged in elaborative interrogation outperformed learners who produced labeled diagrams of the concepts in each cursory text.

Self-explanation is a strategy in which learners produce explanations of material or of their thought processes while they are reading, answering questions, or solving problems. In the most general case, learners may but be asked to explain each step they have every bit they solve a trouble (Chi et al., 1989b; McNamara, 2004) or explain a text sentence-past-sentence every bit they read it (Chi

Suggested Citation:"5 Noesis and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

et al., 1994). Self-explanation involves more than open up-ended prompts than the specific "why" questions used in elaborative interrogation, but both strategies encourage learners to elaborate on the material past generating explanations. Other examples of this work include cocky-explanations of physics.

An early on study of self-explanation was carried out by Chi and colleagues (1994). Eighth-class students learned nearly the circulatory system by reading an expository text. While one group simply read the text, a second group of students produced explanations for each judgement in the text. The students who self-explained showed larger gains in comprehension of concepts in the text. A subsequent study showed like results (Wylie and Chi, 2014). Self-explanation has now been explored in a wide range of contexts, including comprehension of science texts in a classroom setting (McNamara, 2004), learning of chess moves (de Bruin et al., 2007), learning of mathematics concepts (Rittle-Johnson, 2006), and learning from worked examples on problems that require reasoning (Nokes-Malach et al., 2013). Self-explanation prompts have been included in intelligent tutoring systems (Aleven and Koedinger, 2002) and systems with game components (Jackson and McNamara, 2013; Mayer and Johnson, 2010). All the same, relatively few studies have examined the effects of self-explanation on long-term retention or explored the question of how much self-caption is needed to produce notable results (Jackson and McNamara, 2013).

A few studies have explored the relationship between self-explanation and prior cognition in learning (Williams and Lombrozo, 2013). For example, Ionas and colleagues (2012) investigated whether self-explanation was benign to college students who were asked to practice chemistry problems. They found that prior knowledge moderated the effectiveness of cocky-caption and that the more than prior noesis of chemistry the students reported having, the more cocky-explanation appeared to assistance them learn. Moreover, for students who had just a little prior knowledge, using self-caption seemed to impede rather than support functioning. The researchers suggested that learners search for concepts or processes in their prior noesis to brand sense of new material; when the prior knowledge is weak, the unabridged process fails. They ended that educators should thoroughly assess the learners' prior knowledge and use other cognitive support tools and methods during the early stages of the learning process, equally learners strengthen their knowledge base.

Finally, educational activity others tin be an effective learning experience. When learners prepare to teach they must construct explanations, just as they practise in elaborative interrogation and self-caption activities. All the same, elaborative interrogation and self-explanation both require that the learner receive fairly specific prompts, whereas the act of preparing to teach tin can be more than open-ended. Teaching others is frequently an excellent opportunity to strop i's own knowledge (Biswas et al., 2005; Palincsar and Chocolate-brown, 1984), and learners in this kind of interaction are probable to feel empowered and responsible in a

Suggested Commendation:"5 Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

mode that they do not feel when they are the passive recipients of knowledge (Scardamalia and Bereiter, 1993). Peers may exist able to limited themselves to each other in ways that are particularly relevant, firsthand, and informative. Although peer learning and teaching are often quite effective, teachers and instructors typically come closer to injunctive norms and provide ameliorate models to notice.

A foundational study of the effects of teaching on learning past Bargh and Schul (1980) has served equally a template for subsequent studies. Bargh and Schul asked participants to report a set of materials and either prepare to teach the textile to a peer or simply study information technology for an upcoming examination. Both groups were tested on the material without teaching information technology; simply the expectation to teach had been manipulated. Students who prepared to teach others performed better on the assessment than students who only read and studied the material. Effects of preparing to teach take been replicated in studies since Bargh and Schul's foundational work (e.g., Fiorella and Mayer, 2014).

The benefits of teaching are evident in other contexts. For instance, enquiry on tutoring has shown that while students certainly learn by being tutored, the tutors themselves larn from the experience (encounter Roscoe and Chi, 2007). Reciprocal teaching is some other strategy, used primarily in improving students' reading comprehension (Palincsar, 2013;Palincsar and Brown, 1984). In reciprocal pedagogy, students acquire by taking turns teaching textile to each other. The students are given guidance: training in 4 strategies to assistance them recognize and react to signs of comprehension breakup (questioning, clarifying, summarizing, and predicting) (Palincsar, 2013).

The enquiry suggests several possible reasons why education may do good learners. Preparing to teach requires elaborative processing because learners demand to generate, organize, and integrate knowledge. Also, as mentioned, the explanations that people create may promote learning in the same fashion that elaborative interrogation and self-explanations promote learning. The process of explaining to others is active and generative, and it encourages learners to focus on deeper questions and levels of comprehension. Explaining in a educational activity context also involves retrieval practice, as the teacher actively engages in retrieving knowledge in social club to explain instructional content and reply questions. Although researchers have documented benefits of caption, there are cautions to bear in mind. For example, a few researchers in this surface area have noted that in developing explanations learners may tend to make broad generalizations at the expense of significant specifics (Lombrozo, 2012; Williams and Lombrozo, 2010; Williams et al., 2013). Children tend to prefer a single explanation for ii different phenomena (east.k., a toy that both lights up and spins), even when there are two independent causes (Bonawitz and Lombrozo, 2012). Too, when diagnosing diseases based on appreciable symptoms, adults tend to attribute the two symptoms to a single disease, fifty-fifty when it is more likely that there are two dissever diseases (Lombrozo, 2007;

Suggested Commendation:"v Cognition and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

Pacer and Lombrozo, 2017). The tendency to prefer unproblematic, broad explanations over more than complex ones may touch on what people larn and the inferences they describe. For each of the different types of explanation strategies, researchers have noted reasons for educators to plan advisedly when and how they can be used most effectively.

CONCLUSIONS

Learners identify and plant relationships among pieces of information and develop increasingly complex structures for using and categorizing what they have learned. Accumulating bodies of cognition, structuring that noesis, and developing the chapters to reason about the knowledge one has are primal cognitive assets throughout the life bridge.

Strategies for supporting learning include those that focus on retention and retrieval of knowledge too as those that back up evolution of deeper and more sophisticated understanding of what is learned. The strategies that have shown promise for promoting learning assist learners to develop the mental models they need to retain knowledge so they tin can use it adaptively and flexibly in making inferences and solving new bug.

CONCLUSION five-ane: Prior knowledge tin reduce the attentional demands associated with engaging in well-learned activities, and it tin facilitate new learning. Withal, prior noesis can also lead to bias by causing people to not nourish to new data and to rely on existing schema to solve new problems. These biases can exist overcome but only through conscious effort.

CONCLUSION 5-ii: Learners routinely generate their own novel understanding of the data they are accumulating and productively extend their knowledge past making logical connections betwixt pieces of information. This capacity to generate novel understanding allows learners to employ their cognition to generalize, categorize, and solve problems.

Determination 5-3: The learning strategies for which there is evidence of effectiveness include ways to assist students remember information and encourage them to summarize and explain cloth they are learning, too as ways to space and structure the presentation of material. Effective strategies to create organized and distinctive cognition structures encourage learners to go beyond the explicit material past elaborating

Suggested Citation:"v Knowledge and Reasoning." National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. doi: 10.17226/24783.

and to enrich their mental representation of information by calling upwards and applying information technology in various contexts.

CONCLUSION 5-4: The effectiveness of learning strategies is influenced by such contextual factors as the learner's existing skills and prior knowledge, the nature of the textile, and the goals for learning. Applying these approaches effectively therefore requires careful thought about how their specific mechanisms could be beneficial for particular learners, settings, and learning objectives.

Suggested Citation:"5 Knowledge and Reasoning." National Academies of Sciences, Technology, and Medicine. 2018. How People Learn Two: Learners, Contexts, and Cultures. Washington, DC: The National Academies Printing. doi: 10.17226/24783.

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Source: https://www.nap.edu/read/24783/chapter/7