Individuals learn outside of school and throughout their lives. What is taught in kindergarten through grade 12 (K-12) is relatively circumscribed, leaving relatively little room for individual choice. However, outside of formal schooling, what and how much people learn is increasingly directed by their own choices and circumstances. They may choose to pursue some form of postsecondary formal education or career training or move directly to full employment, raising a family, and other pursuits, and they may combine these options in different ways over time. Regardless of the path, each individual’s lifelong development is shaped and constrained by the resources and opportunities afforded in her own complex environment, which is embedded in cultural context, as we discussed in Chapter 2.
The authors of HPL I1 noted that the framework they recommended for K-12 education applied as well to adult learning (National Research Council, 2000). They noted in particular that few professional development programs for teachers met the criteria they outlined for K-12 educational environments. Their report emphasized the importance of the learning context for knowledge transfer but did not elaborate on that point with respect to changes in learning and cognition across the life span.
The learning processes discussed in this report function throughout the life span, but many do change with age, as do the contexts in which people learn and the reasons for engaging in continuous development through their life span. In this chapter, we examine research that addresses learning that
takes place outside of compulsory education settings and the changes that occur across the stages of life. We consider how learning abilities are affected by aging and assess ways to preserve cognitive abilities. We also discuss research on learning disabilities that may affect learners throughout life. We then turn to learning in two environments familiar to adults: postsecondary education and the workplace. The chapter closes with a discussion of ways to foster lifelong learning. For this discussion, we rely on laboratory- and field-based cognitive science research.
Many changes affect learning as individuals age. Changes occur over time in reasoning processes and cognitive abilities. An individual’s knowledge base and motivation for learning also change. These changes reflect variations in the environments in which people learn as they get older and the types of learning activities they are likely to undertake.
Two cognitive resources we discussed in Chapter 5 are particularly important as people age: the reasoning abilities associated with generating, transforming, and manipulating information and the knowledge accumulated through experience and education (the expertise an individual acquires) (Salthouse, 2010). We noted that both reasoning capacity and knowledge accumulation increase up to early adulthood, after which their paths begin to diverge. At that point, reasoning ability begins to decline, while learners retain or increase their base of knowledge as they age. The accumulated knowledge helps learners compensate for the declines in reasoning capacity that come with age.
Research on two knowledge domains that are important to adult learners—personal health and finance—illustrates how existing knowledge facilitates new learning. Researchers tested the hypothesis that older adults who had a base of knowledge about general health could more easily learn new information about heart disease. They found that base knowledge was predictive of new-information retention, particularly for participants who could learn at their own pace, which minimized cognitive load (Beier and Ackerman, 2005). Similarly, prior knowledge of investment products facilitated new learning about managing investments in a self-paced learning environment for adults (Ackerman and Beier, 2006). Very similar results have been found for new learning about technology (Beier and Ackerman, 2005). In general, older people are likely to know more than younger people do, and that knowledge facilitates their learning (Ackerman, 2000; Beier and Ackerman, 2001, 2003, 2005).
This research reinforces a point we made in Chapter 5: as people age and develop expertise in domains associated with their work and other aspects
of life, they rely less on reasoning abilities to learn from the experiences of everyday life. However, with new experiences that are more removed from what a learner already knows, he can rely less on his knowledge base and will likely find the learning more challenging. For instance, it would be more difficult for a lawyer or doctor to learn a completely new career, such as K-12 teaching, if she has reached or passed middle age than at a younger age. She could certainly still make this change, by applying knowledge gained through working with other adults to the new challenge of managing a classroom full of children. She would combine prior knowledge about how best to work with others with feedback from the new environment and determine how to transfer the skills she learned with adults to the new challenge.
Although the changes that occur on average as people age are well understood, questions about age-related trajectories in learning abilities are complicated by individual variability (Hertzog et al., 2008). That is, different individuals would be expected to grow or decline at varying rates, depending on the characteristics of their environments, exposure to pollutants that affect neurophysiological functioning, health and sleep habits, and many other factors. Every individual’s trajectory will be idiosyncratic and depend on his particular experiences with schooling, work, family and community, hobbies, and more. Further, there is not one standard age at which abilities change in a way that affects learning and development. The general age-related trajectories in abilities are a function of regular aging (as opposed to memory impairment that is a function of psychopathology, such as dementia or Alzheimer’s disease).
In Chapter 6, we discussed influences on people’s motivation to learn that apply in general across the life span, but what people value and other aspects of motivation are likely to change as they age. These changes will influence the goals they pursue and the types of activities they perceive to be important to their sense of competence and well-being (Ebner et al., 2006; Kooij et al., 2011). Developmental activities that do not provide the learner with a sense of growth and accomplishment are unlikely to be sustained as people age (Carstensen et al., 1999). For instance, there is evidence that people’s motivation to achieve and to be recognized for that achievement, whether at work or in other environments, tends to decline with age, while their motivation to use their vast repertoire of skills, help others, and preserve their resources and sense of competence tends to increase with age.
Some researchers have suggested that successful aging is a function of selecting age-appropriate goals, optimizing existing resources, and compensating for age-related declines using social or technological resources (Baltes and Baltes, 1990; Heckhausen et al., 2010). Others have pointed out the importance of age-related changes in affective preferences for information dur-
ing learning (Carstensen et al., 1999). For example, one study suggested that older adults were more likely than younger ones to prefer positive emotional information and to avoid negative emotional information (Wang et al., 2015). Specifically, older workers responded favorably to feedback that was positive, perceived to be of high quality, and delivered in a fair manner. They were also more attentive to the interpersonal nature of feedback, whereas younger workers were more attentive to feedback that provided information on how to improve their performance. Compared to younger learners, older learners have also been shown to be more likely to compensate for age-related changes in reasoning and cognitive abilities when they participate in training and development programs that build on existing knowledge, are well structured, and permit learning to occur in less time-pressured formats (Heckhausen et al., 2010; Maurer et al., 2003).
Taken together, this evidence on motivation during adulthood points to the importance of learning opportunities and environments that take account of age-related changes in learner capacities, motives, and affective preferences. Specifically, research points to the value of training for older learners that enhances the learner’s self-efficacy, accommodates age-related differences in cognitive capacities and emotional reactions to feedback, uses content that builds on the trainee’s existing knowledge and skills, and has immediate relevance to the trainee.
The environments in which people learn also vary as they progress through the life span. Learning in adulthood may occur in connection with formal programs aimed at professional development or when an individual pursues or improves skills such as mathematics literacy or English as a second language. Learning also occurs in connection with the desire to develop avocational interests or to improve health and financial literacy to deal with the challenges of daily life (Kanfer and Ackerman, 2008). Thus, adult learners may be engaged in formal learning environments, such as when a manager working full time enrolls in a continuing education course to learn more about art history in her spare time or when an unemployed maintenance worker engages in workforce software training. But much adult learning—whether in personal life or on the job—takes place in informal training environments: for instance, in learning a new job by executing its tasks without formal training (Tannenbaum et al., 2010) or in the learning that takes place when a person visits a new city or country, reads a newspaper, or plans for retirement.
We also note that people generally adapt their ideas about what they want to learn and do in the future as they age (Carstensen et al., 2003), and they tend to choose environments that align with their established knowledge and
skills, which makes learning new domain-related information easier (Baltes and Baltes, 1990). Figure 9-1 provides a framework for thinking about the types of learning and development activities in which a person might engage over the life span. It highlights whether the activity is done of the learner’s own volition (willingly chosen) and the formality of the learning environment (the extent to which the activity is structured and specifies desired learning outcomes). This figure is useful for framing a discussion of learning environments, but distinctions may not always be clear-cut. For example, if particular training is necessary for a desired promotion, pursuing this training opportunity could be viewed as either autonomous or mandated.
The decoupling of the formality with which training content is delivered from its volitional nature, as shown in Figure 9-1, will likely become increasingly important with the proliferation of educational technology, which has increased access to affordable, self-directed training and development activities at different levels of formality. Participation in such activities may be motivated by an individual’s desire to develop workplace skills for a promotion or job change, but they generally include learning goals, a schedule, a curriculum, and possibly a syllabus (Siemens et al., 2015). According to the National Center for Education Statistics, in 2005, more than 39 percent of adults ages 40 to 65 had participated in some form of formal coursework during the previous
12 months,2 and with the proliferation of online learning experiences such as massive open online courses, the number of people engaging in formal coursework should increase even further. Further work is needed to examine all of the components of lifelong intellectual development.
There can be substantial variability in the trajectory of people’s cognitive ability as they age. At one extreme are “super agers,” who perform like younger adults and often have brains that resemble those of people two to three decades younger (e.g., Harrison et al., 2012). At the other extreme are older adults with mild cognitive impairment or dementia. Although coverage of this latter portion of the spectrum is beyond the scope of this report, there has been an increasing interest in examining the factors that may explain some of the variance in functioning among older adults and in using structural and functional neuroimaging methods to better identify what neural differences may relate to that variability in performance (Kensinger, 2016).
Age-related changes in cognition affect the way adults process and maintain information and therefore also affect how adults learn. Although cognitive declines are relevant to learning—because these abilities include the attentional and cognitive resources a person can devote to learning and intellectual development—they are not the same as learning. In adults, the ability to generate and contribute to a knowledge base increases until their 60s and then gradually declines. However, when cognitive abilities are examined separately, a varied age-related trajectory can be seen. In terms of memory, some abilities (e.g., binding pieces of information together in memory, the ability to provide specific memories, metamemory during retrieval) show relative decline with aging while others (collaborative memory, emotional and motivated memory, acquisition and maintenance of existing knowledge base) show relative preservation with aging.
In Chapter 3, we discussed the ways adult brains may compensate for declines in some kinds of cognition by recruiting other resources. Although late adulthood has been associated with decreases in the cognitive abilities associated with learning novel information, memory, and speed, this stage has also been associated with increased skill in solving social dilemmas (Grossman et al., 2010). One interpretation of this increase is that older people may be better able than younger people to evaluate the negative consequences of social decision making. Another view is that older adults focus on the bigger picture of how social conflicts relate to the broader values and feelings of those involved—a shift that can be described as growing “wise” and that plays an important cultural role in society.
2 See https://nces.ed.gov/programs/digest/d14/tables/dt14_507.30.asp [March 2017].
In Western contexts, ideas of “successful aging” (Havighurst, 1961) have incorporated concepts of social engagement as well as cognitive function (Rowe and Kahn, 1987). As a result, there has been increasing interest in understanding why the social connectedness of an individual could influence age-related trajectories (see Antonucci et al., 2001; Berkman, 1985, for reviews). Research has confirmed that factors such as life satisfaction (Waldinger et al., 2015) can mitigate some of the declines associated with aging. Similarly, having a strong social network (Glymour et al., 2008) rather than being lonely (Wilson et al., 2007) can reduce the speed of age-related cognitive decline (Kensinger, 2016).
Some effects of aging can be thought of as interactions between an individual and an environment that unfold over time. These interactions can manifest in two ways. First, age can minimize or exaggerate the effects of culture. For instance, differences in how American and Chinese people categorize information are larger among older adults than among younger adults (Gutchess et al., 2006). Even though this research was cross-sectional (i.e., studying all age groups at once), this result suggests that aging magnifies cultural differences—likely because of the additional time that an older adult has been immersed in the culture. Alternatively, it may indicate a historical change: that cultural differences between these groups were more pronounced at the time when the older participants were young. Conversely, cultural effects may sometimes be minimized with aging; this pattern is thought to occur because effects of culture are minimized as resources become depleted with age (Kensinger, 2016; Park and Gutchess, 2002).
Culture can also influence the types or degree of cognitive changes that are manifested with age. Researchers have explored this idea by examining the effects of more localized environments or subcultures on cognitive aging and asking how the community environment affects the way that cognition changes as a person ages, but these investigations have not yet established a clear answer. That is, effects have been noted in a number of studies, but the magnitude of the effects, as well as the specific domains showing the largest effects, have varied from study to study (e.g., Cassarino et al., 2015; Wu et al., 2015). Moreover, the intersection between the influence of community, social support, and social networks has been under-explored. Although there is still much to learn, the extant research does suggest that community environment, in addition to broader cultural influences, will need to be considered in order to understand the reasons for variation in cognitive aging trajectories (Kensinger, 2016).
We look next at disabilities that may affect learning at every age. A conservative estimate is that 2 to 5 percent of children in the public school popula-
tion have learning disabilities, and they are the largest category of children served in special education. However, there is no agreed-upon definition of learning disabilities that applies to adults, so there are not firm estimates of the percentage of U.S. adults who are affected by them (Lindstrom, 2016; Swanson, 2016).
Learning disabilities have been defined as “unexpected, significant difficulties in academic achievement and related areas of learning and behavior in people who have not responded to high-quality instruction” and whose difficulties “cannot be attributed to medical, educational, environmental, or psychiatric causes” (Cortiella and Horowitz, 2014, p. 3). It is important to emphasize that learning disabilities are not the result of poor instruction. They are caused by specific psychological processing problems; neurological inefficiencies with a biological base affect performance on specific tasks such as the acquisition and use of listening, speaking, reading, writing, reasoning, or mathematical abilities, specifically:
- The difficulty is not the result of inadequate opportunity to learn, general intelligence, or significant physical (e.g., hearing impairment), emotional (e.g., stress), or environmental factors (e.g., poverty, family abuse), but of basic disorders in specific psychological processes (such as remembering the association between sounds and letters).
- The difficulty is not manifested in all aspects of learning. The individual’s psychological processing deficits depress only a limited aspect of academic behavior.
The most common types of learning disabilities are those that affect learning in reading, mathematics, or written expression. Dyslexia, which is difficulty reading that results from problems in identifying speech sounds and learning how they relate to letters and words, is the most prevalent and easily recognized type of learning disability. Individuals who have disabilities in reading may also have other disorders of attention, language, and behavior, but each affects learning in a different way (Cortiella and Horowitz, 2014). Though learning disabilities share certain features, there is a great deal of variability among the individuals affected by them (Swanson, 2016).
Learning disabilities arise from neurological differences in brain structure and function and affect a person’s ability to receive, store, process, retrieve, or communicate information. While the specific nature of these brain-based disorders is still not well understood, considerable progress has been made in mapping some of the characteristic difficulties to specific brain regions and structures. Evidence suggests that some learning disabilities have a genetic basis. Researchers have documented, for example, that certain learning dis-
Learning disabilities may also be a consequence of insults to the developing brain that occur before or during birth, such as significant maternal illness or injury, drug or alcohol use during pregnancy, maternal malnutrition, low birth weight, oxygen deprivation, and premature or prolonged labor. Postnatal events resulting in learning disabilities might include traumatic injuries, severe nutritional deprivation, or exposure to poisonous substances such as lead.
We emphasize that a learning difficulty is not a learning disability if it is caused by visual, hearing, or motor disabilities; intellectual disabilities (formerly referred to as mental retardation); emotional disturbance; cultural factors; limited English proficiency; environmental or economic disadvantages; or inadequate instruction. However, according to Cortiella and Horowitz (2014), there is a higher reported incidence of learning disabilities among people living in poverty, perhaps because of increased risk of exposure to poor nutrition, ingested and environmental toxins (e.g., lead, tobacco and alcohol), and other risk factors during early and critical stages of development. Moreover, given that learning is affected by a complex set of environmental and individual variables, the stigma of learning disabilities is likely to also affect continuing growth and development throughout the life span (Lindstrom, 2016). Here we focus on two subtypes of learning disabilities that have been extensively researched: reading and math disabilities.
It is difficult to know exactly how many children and adolescents are affected by disabilities in reading because available data are not broken down by type of learning disability.3 Researchers have identified three types of reading disabilities (Flecher et al., 2007): (1) problems in word recognition and spelling; (2) difficulties in reading comprehension; and (3) difficulties in reading fluency and poor automaticity of word reading.4 Although there are no population-based studies of this disorder, individual studies suggest that approximately 10 percent of samples of children with reading problems have
3 In 2014-2015, 13 percent of public school students received special education services, and 35 percent of those students were classified as having some type of learning disability (see https://nces.ed.gov/programs/coe/indicator_cgg.asp [June 2017]). In 2013, the percentage of children identified by a school official or health professional as having a learning disability was 8 percent (see https://www.childtrends.org/indicators/learning-disabilities [June 2017]). The terms “reading disability,” “dyslexia,” and “specific learning disorders in reading” are used interchangeably. Most researchers who focus on anatomical abnormalities use the term “dyslexia,” whereas researchers interested in cognitive dysfunction use the term “reading disabilities” (Swanson, 2016).
Research suggests that fundamental deficits in verbal abilities (including but not limited to reading disabilities) emerge between the ages of 5 and 18. These findings align with neurological studies that suggest that underactivation of certain brain regions correlates with weak cognitive performance on verbal tasks (Maisog et al., 2008; Richlan, 2012; Richlan et al., 2009, 2013). Because most neuroimaging studies of reading disabilities have been conducted with children or adults who have had years of reading difficulty, it has been impossible to determine whether the brain differences are associated with the underlying neurobiological causes of reading disabilities or are instead the consequence of years of altered and often vastly reduced reading experience (including compensatory alterations in reading networks) (Lindstrom, 2016). However, a variety of research supports the conclusion that underlying brain physiology accounts for some reading disabilities (Fischer and Francks, 2006; Hoeft et al., 2007; Leppänen et al., 2012; Molfese, 2000; Neuhoff et al., 2012; van Zuijen et al., 2013).
Although mathematics disabilities have been less thoroughly researched than reading disabilities, they are also common.5 The fact that some children have disabilities in both areas suggests that a similar cognitive deficit can play a role in both (Geary, 1993, 2013). Like other learning disabilities, those disabilities specifically affecting mathematics learning, often referred to as dyscalculia, are neurodevelopmental disorders of biological origin (American Psychiatric Association, 2013).
A synthesis of the literature on mathematics disabilities (Geary, 1993; also see Geary, 2013, for a review) identified three distinct groups of children with mathematics disabilities. One group is characterized as deficient in semantic memory. These children have disruptions in the ability to retrieve basic facts from long-term memory and high error rates in recall. Further, the characteristics of these retrieval deficits (e.g., slow solution times) suggest that children in this first group do not experience a simple developmental delay but rather have a more persistent cognitive disorder across a broad age span (Swanson, 2016).
Children in the second group have procedural types of math disabilities.
5 As with reading disabilities, measuring the prevalence of math disabilities is challenging. Estimates ranging from 3 to 6 or 7 percent of the school-age population have been suggested (e.g., Geary, 2013; Reigosa-Crespo et al., 2012) but definitions vary. A significant number of children in U.S. schools demonstrate poor achievement in mathematics, and it is likely that disabilities account for some of that deficit (Swanson, 2016).
They generally use developmentally immature procedures in numerical calculations and therefore have difficulties in sequencing multiple steps in complex procedures. Children in the third group have a visual/spatial math disorder. These individuals have difficulties representing numerical information spatially. For example, they may have difficulties representing the alignment of numerals in multicolumn arithmetic problems; may misread numerical signs; may rotate or transpose numbers; may misinterpret spatial placement of numerals; and may have difficulty with problems involving space in areas, as required in algebra and geometry (Lindstrom, 2016).
Children with math disabilities, in contrast to learners characterized as low achievers, show a deficit in number processing, learning of arithmetic procedures, and memorizing basic arithmetic facts. Further, children with math disabilities do not necessarily differ from their peers with normal math ability in the types of strategies they use to solve simple arithmetic problems. However, they do differ in the percentage of retrieval and counting errors they make as a result of incorrect long-term memory of addition facts and lower average working memory capacity. Children with math disabilities have pervasive deficits across all working memory systems, but understanding of the relationship between specific components of working memory and specific mathematical cognition is still in the developmental stages (Geary, 2013; Swanson, 2016).
Few common patterns in anatomical causes of dyscalculia, or math disabilities, have been identified. However, in a meta-analysis of magnetic resonance neuroimaging studies of children diagnosed with developmental dyslexia and/or math disability, Kaufmann and colleagues (2011) found that children’s activation patterns were modulated by the type of task performed (symbolic or nonsymbolic; number comparison versus calculation). These findings suggest both areas of commonality and differences; additional research to explore these connections would be useful.
There is no single, shared method for assessing and counting adults with learning disabilities related to literacy or math skills (Fletcher, 2010; Gregg et al., 2006; MacArthur et al., 2010; Mellard and Patterson, 2008; Sabatini et al., 2010; Swanson, 2016). Thus, it is unclear how many adults have learning disabilities in either area. A conservative estimate is that approximately 3 to 5 percent of the general population has a reading disability (Swanson, 2016). Looking more broadly, it has been estimated that 20 to 30 percent of U.S. adults lack the literacy skills needed to meet the reading and computation demands associated with daily life and work (Kutner et al.,
Because there is limited research on reading disabilities in adults (and even less on adults’ mathematics disabilities), it is unclear whether adults with reading disabilities have cognitive deficits similar to those that have been noted in children or whether adults’ cognitive deficits are the result of other factors, such as relatively lower general intelligence compared to adults not suffering from reading disabilities. In one examination of these issues, Swanson and colleagues (Flynn et al., 2012; Swanson, 2012; Swanson and Hsieh, 2009) synthesized research in which adults with reading disabilities were compared with average-achieving adult readers to determine how they differ from adults without a reading disability on measures related to overall reading competence. These researchers found differences in reading comprehension, reading recognition, verbal intelligence, naming speed, phonological awareness, and verbal memory (Swanson and Hsieh, 2009; Swanson, 2016).
There is also little research on the social and other consequences for adults who have learning disabilities. Existing research mostly focuses on the transition from secondary schooling into the workforce. Researchers have found that, compared to their nondisabled peers, adults with learning disabilities have a greater risk of dropping out of postsecondary schooling (Newman et al., 2009; Rojewski et al., 2014, 2015), lower postsecondary enrollment and attainment (Wagner et al., 2005), restricted labor force participation (Barkley, 2006), and lower earnings (Day and Newburger, 2002). The majority of jobs obtained by adolescents with learning disabilities when they leave school are semiskilled and usually part-time positions (Barkley, 2006; Gregg, 2009; Rojewski, 1999). Although some research (Newman et al., 2010) shows no real differences in earnings for these young people, even when wages were adjusted for inflation, there is evidence that the earning power gap between learning-disabled adults and their nondisabled peers is widening as a result of growing disparities in educational attainment (Day and Newburger, 2002; Swanson, 2016; Wagner et al., 2005).
Many adults in the United States and around the world lack basic literacy skills. U.S. adults scored below average in a study of literacy, numeracy, and
6 The Program for the International Assessment of Adult Competencies (PIAAC) collects data on adult literacy and reports it in terms of percentages of adults who score at five different proficiency levels in these areas (see https://nces.ed.gov/surveys/piaac/results/makeselections.aspx [June 2017]).
problem solving in technology-rich environments conducted in 22 countries across Asia, North America, Europe, and Australia (Goodman et al., 2013). More than 50 million adults in the United States do not read at a level sufficient for them to secure a job, yet only a small percentage of these adults (approximately 2 million) were enrolled in federally funded adult education programs to increase their skills (National Research Council, 2012c). Even when adults do enroll in adult education, literacy programs are beset with many obstacles: poor funding; limited professional development for teachers and tutors; high absenteeism and attrition rates; and a wide diversity of students in terms of racial, ethnic, and gender identities, and age (between 16 and 80+), as well as employment, educational, and language status (Greenberg, 2008).
Technology is a key tool for providing access to adult education for learners who have work and family responsibilities that make attending courses in person difficult. Technology also makes it easier to tailor training to suit diverse skills and reading levels (Kruidenier, 2002; National Institute of Literacy, 2008). Adaptive, intelligent tutorial programs can address a range of skills and needs, and programs available online allow students to access the learning environments in their own homes, neighborhood libraries, schools, houses of worship, or locations of employment. Technology can also be used to develop environments that motivate learners, such as social media platforms, computer systems with intelligent conversational agents, and Web-based repositories of readings that target the particular interests of the adult (National Research Council, 2012c).
A significant body of research on adults who read at the third- to eighth-grade levels is available at the Center for the Study of Adult Literacy (CSAL).7 The research explores interventions to improve reading that can be implemented by teachers or tutors or by means of computer technologies. For example, one promising intervention is based on a successful teacher intervention called PHAST-PACES, which focuses on obstacles to word identification and decoding through a framework of phonologically based remediation (Lovett et al., 2012). CSAL tailored the program, which uses a combination of direct instruction and dialogue-based metacognitive training for adult readers.
We emphasize that interventions to improve adult literacy must optimize a number of factors to be successful; it is important to also consider the prospective participants’ motivation, emotions, interests, and social lives, so that the materials used in the intervention have practical value for their lives.
Adults and children with learning disabilities are a diverse group, and no general instructional model can be recommended for all of them (Swanson,
2016). With respect to children, there have been several meta-analyses that examined instructional interventions in domains such as mathematics (e.g., Gersten et al., 2009; Xin and Jitendra, 1999), writing (Graham and Perin, 2007), and reading (e.g., Berkeley et al., 2010; Edmonds et al., 2009; Swanson, 1999; Wanzek et al., 2013).
The results from these studies suggest that children with learning disabilities are generally responsive to intense instruction. For example, controlled experimental studies showed a relatively large improvement after intense instruction using particular models (Swanson et al., 1999). These interventions involved (a) teaching a few concepts and strategies in depth rather than teaching a larger number superficially, (b) teaching students to monitor their performance, (c) teaching students when and where to use the strategy in order to enhance generalization, (d) teaching strategies as an integrated part of an existing curriculum, and (e) providing supervised student feedback and opportunities for practice. The results indicated that explicit strategy instruction (explicit practice, elaboration, strategy cuing) and small group interactive settings yielded the greatest improvement in treatment outcomes (Swanson, 2000).
One might expect these findings to generalize to populations of adults with learning disabilities, but this remains an area ripe for future research (Flynn et al., 2012; Hock, 2012; Mellard and Patterson, 2008; Swanson, 2016). Most of the available work related to adults has been limited to identifying assessment accommodations (e.g., providing extended time for testing) for adults with learning disabilities. Because there is limited research on learning disabilities in adults (including assessment tools), it is unclear whether the cognitive deficits seen in children are similar to those in adults with learning disabilities. Although no general instructional model can be recommended for all adults and children with learning disabilities, children are generally responsive to intense instructional programs.
We note also that many of the difficulties associated with learning disabilities such as dyslexia, dyscalculia, and attention deficit disorders stem from a mismatch between individuals’ neuropsychological predispositions and strengths on the one hand and the demands of the learning context on the other (see e.g., McDermott and Varenne, 1996). For example, dyslexic learners’ phonological decoding deficits (i.e., problems associating letters with linguistic sounds) are especially problematic in countries that use phonographic (alphabetic) writing systems with complex orthographic conventions, such as English. In countries such as Greece and Germany where the orthographic conventions are more straightforward, there is a lower incidence of dyslexia (Landerl et al., 2013; Vellutino et al., 2004).
The match between learner and context is critical to good outcomes: in conducive contexts and with the right supports, students with learning disabilities and mental illnesses can be successful students. For example, there is
evidence that allowing students with attention deficit/hyperactivity disorder to structure their learning environments and resources adaptively can facilitate their learning (Fugate et al., 2013). Individuals with disabilities reflect the same range of human qualities and abilities that others do. Education that capitalizes on an individual student’s capacities that are assets for learning (e.g., a dyslexic learner’s strengths in pattern recognition and peripheral vision) is thus particularly important for these students (Lorusso et al., 2004; Schneps et al., 2007; von Károlyi et al., 2003; see also Wei et al., 2013, regarding individuals with autism).
Once people complete their compulsory education, they may pursue further education in a variety of settings (e.g., community college, college, university, vocational or technical schools). There are several important differences between K-12 and postsecondary education settings.
First, institutions that educate adults have varied goals. Many academic institutions use prior academic performance and ability to select those they think will succeed and thrive in the academic environment they provide; they do not have responsibility for the success of people whom they do not accept or who do not succeed in their environments. Although there are exceptions, such as adult literacy and retraining programs, for most academic institutions and organizations that are training employees the focus is on recognizing and rewarding talent, rather than raising the performance of those who are struggling. Though academic institutions and educators are increasing their attention to factors that affect their students’ performance and persistence (such as adjustment to college life and study skills), it remains true that when students do not perform well in school, colleges are not required to continue to enroll them. In work environments, the outcomes for people who are not able to learn new skills can be even harsher; workers who cannot or will not learn a required skill can expect to be told to look for other employment. These two examples illustrate how vital it is that K-12 experiences prepare students for the developmental demands of college and beyond.
There are also marked differences between the classroom experiences characteristic of K-12 and postsecondary education and those common in training and development in the workplace. In postsecondary situations, students may be expected to complete more of the work outside of the classroom than they had in high school, but they are free to decide how they will prioritize their study time and get work done. In work situations, supervisors will rarely assess whether the employee has learned the necessary skills to execute a task; rather, workers are expected to figure it out on their own and ask questions if they have them. This increased autonomy highlights the importance of interest, motivation, and the capacity to monitor and regulate their own progress.
Researchers have not directly assessed the relative importance of interest and motivation among K-12 and postsecondary students. However, there is some empirical evidence that these factors are important for success in postsecondary environments, along with cognitive ability and psychosocial contextual influences such as cultural background (e.g., status as a first-generation college student8 (see Ackerman et al., 2013; Richardson et al., 2012). Most of this research uses grade-point average as a proxy for learning, though many factors may affect it. This research suggests that cognitive ability (typically measured through standardized tests) and high school performance tend to account for the most variance in college grade point average, but motivational factors such as academic self-efficacy, intrinsic motivation, and goal orientation also have been positively associated with academic performance (Ackerman et al., 2013; Richardson et al., 2012).
Researchers have also begun examining elements of a student’s pre-college experiences and cultural background to better understand the factors that lead to success. They have found that the social climate at many colleges and universities does not serve minority and first-generation students well (Stephens et al., 2012). These students often encounter challenges that other students do not face. First-generation students, for instance, tend to come from families
8 A first-generation student is a student who does not have at least one parent who graduated from a 4-year college or university.
with far fewer resources than continuing-generation students, so they are more likely to work for pay at one or more jobs during college in order to pay tuition or living expenses (Phinney and Haas, 2003). They therefore have less time to invest in further opportunities for learning and development, such as unpaid internships (Pascarella et al., 2004).
These challenges are also cultural in that many American universities support middle-class norms of independence (e.g., paving one’s own path), which can be at odds with working-class norms of interdependence (e.g., connecting with others; attending to others’ needs). Studies of the possible effects of a cultural mismatch for first-generation students suggest that positioning the university culture as independent rendered tasks more difficult for first-generation students, but that representing the culture as interdependent facilitated their performance (Stephens et al., 2012). This is but one example, and although research continues to examine elements of postsecondary educational environments that facilitate or impede student performance, more work in this area is urgently needed. Box 9-1 describes an approach to addressing this problem.
Formal training accounts for a relatively small percentage of workplace learning, but it is still important for many learners (Tannenbaum et al., 2010). Developing an effective training program requires attention to the needs of the organization and its employees, as well as the constraints in which the organization operates (Goldstein and Ford, 2002).
Some research has examined training performance as people age. Age is generally negatively related to performance in training, in that older learners typically take longer in training and do not perform as well as younger learners after training (Kubeck et al., 1996; Ng and Feldman, 2008). Nonetheless, research does indicate that older adults can learn in training environments if that environment is designed to meet the individual needs of learners (Callahan et al., 2003; Charness and Schumann, 1992). The bottom line is that tailoring instruction to the different motivations and abilities of individual learners is important for workplace training for people of all ages, and the same training intervention will not be equally effective for everyone (Cronbach, 1957; Snow, 1989). The age-related differences in performance that should be considered in planning training for older adults likely relate to the changes in reasoning and motivation discussed above.
Although very little research has examined tailored instruction with working-age adults, the available evidence suggests that older learners may benefit from more structure (i.e., step-by-step instruction) in highly complex training environments (Carter and Beier, 2010; Gully et al., 2002). Nonetheless, research in this area is sparse, and much work remains to be done to identify
the best training interventions for individual learners at any age. However, we note that technological advances in training design offer new and easier means of customizing training to individual learners’ needs and interests (Snow, 1989; Wolfson et al., 2014). For instance, an employee using an online training tutorial can modify the structure of the program to meet her needs by changing the interface to provide step-by-step instructions when the content to be learned is unfamiliar and perhaps change it back to provide less instruction when the knowledge domain is more familiar. Technology to support tailored instruction has great promise for workplace training and is a topic that merits further research (Gully and Chen, 2010; Wolfson et al., 2014).
The effectiveness of workplace training is typically assessed in four ways that derive from an evaluation framework designed to assess an array of outcomes, from trainee reactions to the organization’s return on investment (Alliger and Janak, 1989; Kirkpatrick, 1967). First, immediately after training, surveys and other methods can be used to assess trainees’ reactions to and satisfaction with different aspects of the training. Second, an evaluation, typically a knowledge test, can be conducted directly after the training has concluded to measure the knowledge acquired by each trainee. Third, the extent to which the trainee has transferred what was learned in training back to the workplace can be assessed, usually by examining workplace behaviors after training has concluded.
The fourth indicator, which can also be measured, is the extent to which the organization benefits over time from the investment in training. Although calculating return on investment can be a complicated process because of the number of different variables other than training (ranging from market trends to myriad organizational initiatives) that can affect organizational success, it is an important outcome for organizations. In 2014, organizations spent an average of just over $1, 200 per employee on training and development activities. From the organization’s perspective, this money is wasted if trainees do not apply what they learn in training to their performance on the job (Goldstein and Ford, 2002). The economic benefits of employee training may be difficult to assess but it is possible to measure trainees’ learning and ability to transfer what they have learned to new situations (Alliger and Janak, 1989).
These four indicators may not provide very complete answers about the effectiveness of training. For example, they may more readily capture trainees’ attitudes or capacity to repeat what they just heard in training, rather than actual learning. The third level of assessment, measuring what the employee transfers to the job, arguably comes closest to assessing learning. But this type of assessment is more challenging than assessing attitudes and knowledge directly after training, so it is used less frequently. These challenges are similar
to the challenges of assessment in other educational settings: it is easier to find out how much students enjoy classes and perhaps what they know on the last day of class than to assess what learning really sticks.
The transfer of training, or applying what has been learned in the workplace, is the third level of evaluation discussed above. It has also been widely studied by cognitive psychologists, who have developed a taxonomy that distinguishes between near transfer and far transfer (Barnett and Ceci, 2002). Near transfer is using a skill learned in training at another time, outside of the training environment. Far transfer is using a trained skill in combination with other elements and/or at a time distant from training. Transfer of skill learning has been studied in other contexts, and findings from that research apply in organizational training contexts. For instance, knowledge learned in training will be more likely to transfer if the training and transfer environments are similar and if the training introduces desirable difficulties (those that pose a manageable level of challenge to a learner but require learners to engage at a high cognitive level) (Schmidt and Bjork, 1992). However, features of the organizational environment, such as how supportive managers and coworkers are when an employee uses a newly learned skill, influence transfer of workplace training back to the job (Blume et al., 2010; Rouiller and Goldstein, 1993).
A meta-analysis of research on workforce training transfer identified characteristics of learners, such as differences in ability or personality (e.g., conscientiousness, dependability) and the training environment that are positively associated with transfer of training (Blume et al., 2010). The authors point to three elements in the work environment that are important:
- Environmental support for training, including peer and supervisor support
- Transfer climate, in the form of implicit cues in the environment that using what is learned in training on the job is expected, such as peers who actively transfer their new knowledge
- Organizational constraints, such as lack of autonomy and other situational factors
They found that environmental support has the largest effect on transfer; organizational constraints also had a modest effect. They found modest evidence that supervisor support in using the new skill may be more important than peer support.
A few examples illustrate how situational cues and opportunities affect training transfer (Blume et al., 2010; Rouiller and Goldstein, 1993). If an employee attends training in the use of a database management software pro-
gram that he rarely uses on the job, and if he does not have the opportunity to practice the newly learned skills for months, he likely will not be able to effectively transfer what he learned to his work. On the other hand, signals in the environment (situational cues) from coworkers or managers can support the employee in using his new skills. These signals may be perceived by the trainee as consequences: if the employee feels that his attempts to use the new skills or tools learned in training are met with negative consequences, he will be less likely to practice the newly learned skill (Blume et al., 2010; Rouiller and Goldstein, 1993). For example, a new database management software designed to streamline a process that had previously involved multiple spreadsheets may feel difficult, inefficient, and error-prone when the newly trained employee first uses it. Indeed, if the difficulty of implementing this new skill is not acknowledged by supervisors and coworkers and the use of the new program similarly encouraged, the employee may revert to the old approach to meet a deadline. This would be an unfortunate waste of organizational resources. Furthermore, research on skill learning suggests that difficulty using newly trained skills—at least initially—should be expected, but after extensive practice people can be expected to execute complex tasks with expertise (Ackerman, 1988; Anderson, 1982).
Because workplace learning is diverse, professionals may engage in learning that is incidental and informal (i.e., as a side effect of the work), intentional but nonformal (related to work activities), or formal on-the-job and off-the job training and education (Tynjälä, 2008). Self-directed, or autonomous, learning at work is the most commonly reported approach for workforce development, but informal methods, such as on-the-job training and peer learning, are largely unstudied (Ellingson and Noe, 2017). One reason is that learning and development are ubiquitous throughout the career span: people often do not realize that the activities in which they are engaging are developmental. Informal development activity is often considered to be a “part of the job” (Tannenbaum, 1997). Such experiences might include learning from failure, mastering new tools to be more efficient at work, or taking on challenging job roles required by a new project. Because learners tend not to view such activities and events as learning experiences, systematic evaluation of this learning is difficult (Boud and Middleton, 2003).
The prevalence of autonomous workplace learning reflects the ways many kinds of careers have changed in industrialized countries over the past 50 years or so. Organizational researchers have remarked that during the mid to late 20th century, many workers could expect to spend the majority of their careers in a single organization and often to retire with a pension plan that rewarded their loyalty. Global competition for the best talent and increased
life expectancies, which have extended the average amount of time a person could expect to live past retirement from less than 10 years to more than 20 (Hall and Mirvis, 1995, 2013), have spurred changes in perceptions of a career. Career growth is now exclusively the responsibility of the individual worker, not the organization. Today, most workers can expect to have multiple jobs and even several careers, pursued at many organizations, over the course of their working life. Assuming equal access to opportunities and no age-related bias, people may shift in and out of the labor pool at any age they wish. Successful navigation of a career now requires continuous learning and development, as these contribute to the development of professional skills, interests, and career identity (Hall and Mirvis, 1995, 2013). Organizational scientists call this phenomenon the Protean career to reflect its ever-changing nature (Hall and Mirvis, 1995).
The shift to Protean careers highlights the important effects that characteristics of both the individual and the environment have on work trajectories. Individual characteristics, such as worker ability, interests, attitudes, and motivation, will play an increasingly important role in learning and development throughout the span of an individual’s working life because of the need to evolve and adapt. The nature of life-span development is essentially individual and is thus driven by each worker’s expectations, decisions, interests, persistence, and abilities. These individual factors also interact with contextual factors both at work (e.g., climate and opportunities for development) and outside of work (e.g., life demands outside of work that make it difficult to participate in skill development) to influence continuous learning (Ackerman, 2000; Beier et al., 2017). Consider, for example, how lack of access to child or elder care, libraries, and community events, and even a reliable Internet connection, may interfere with self-directed learning. Access to both formal (e.g., community education programs and participation in massive open online courses) and informal (e.g., books, Websites, and people-networks) opportunities can greatly facilitate self-directed learning (Comings and Cuban, 2000). The effects of support or barriers are not trivial: for example, the support of a spouse, partner, or parent can be more important than career interest and goals in determining whether or not an individual spends time outside of work to develop new skills (Lent et al., 2000; Tang et al., 1999).
Persistence during learning can also be affected by interactions between the environment and individual factors. For instance, an individual who perceives herself as having declining memory abilities with age may be less likely to participate in learning a new job-related skill after a layoff (Maurer et al., 2003). Any environmental barrier to the developmental experience (i.e., lack of Internet connection or limited transportation to attend training) will make participation in developmental activity even less likely.
Research on self-regulation of learning provides another lens for thinking about individuals’ workplace learning. For example, a qualitative study
examined how knowledge workers in a multinational energy company set and attained their learning and developmental goals to complete a specific project or task (Margaryan et al., 2013). The researchers found that the participants tended to focus on outcome goals (short- and long-term organizational needs relating to the project) rather than process goals, and they tended to be responsive to input from supervisors, mentors, and colleagues when planning and attaining their learning goals. The authors concluded that the participants’ direction of their own learning was highly dependent on the social and organizational context.
This work suggests that the organizational environment, or the organization’s culture for learning, can play a key role in facilitating employee development (Tannenbaum, 1997). The following are important cultural elements that foster continuous workplace development:
- Promoting a “big picture” perspective from which employees know what the goals of the organization are. This enables workers to align development with organizational goals.
- Providing assignments that permit people to stretch beyond their job description. In learning organizations, people are assigned tasks that provide opportunities to do new things, learn new skills, and apply what they learn back on the job (Ford et al., 1992; Schoorman and Schneider, 1988).
- Fostering a climate where people can learn from their mistakes. In learning organizations, mistakes are tolerated, particularly when people are trying new things in the early stages of learning. Research suggests that error-prone practice can actually enhance learning, so if mistakes are tolerated they can lead to greater development (Keith and Frese, 2008).
- Making employees accountable for their own development. For example, performance evaluations might include ratings for engaging in autonomous career-related professional development.
Another effect of the shift to the Protean career model is that because learning is increasingly a function of individual experiences not controlled by an organization, workers’ development is increasingly idiosyncratic. This makes it extremely difficult to conduct any systematic evaluation of autonomous workplace learning and development activity. Nonetheless, the benefits of autonomous learning could be examined on the organizational level by tracking the amount, type, and quality of autonomous learning that occurs within an organization over a period of time and correlating these factors to outcomes such as employee capabilities, retention, and employee perceptions about the culture for learning and development (Tannenbaum et al., 2010). Quantifying the benefits of autonomous workplace learning at the organizational level in
this way would help to clarify how it works and why it is beneficial; thus far, however, we are not aware of any research on this.
Although there has been little scientific research on life span learning and development, the importance of autonomous learning is evident to workers themselves. Qualitative research on communities of practice (i.e., workers with common professional interests) within organizations suggests that employees tend not to rely on opportunities for formal training experiences for their own development unless they are interested in a job-specific skill. Instead, workers explore autonomous development opportunities based on their own interest, motivation, and abilities, as well as the people, resources, and time available in their work and home environments (Boud and Middleton, 2003). Indeed, self-initiated learning is pervasive among adult workers. A survey of more than 400 workers across an array of professions identified learning from coworkers and peers, on-the-job training, trial and error, and observing others as the most common methods of workplace learning; classroom learning at college or formal organizational training were far less commonly cited as important for development at work (Tannenbaum, 1997).
With respect to on-the-job training, organizational scientists have studied the effects of jobs themselves. Jobs, particularly those that are cognitively challenging and that afford workers some control over the tasks in which they engage, offer their own learning opportunities (Hackman and Oldham, 1976; Karasek et al., 1998; Morgeson and Humphrey, 2006). Jobs high in autonomy require employees to make decisions about work methods, work scheduling, and overall decision making, rather than relying on the organization for directives. Jobs high in complexity are challenging, intellectually stimulating, and engaging. Although most research has focused on examining the effect of job characteristics on workplace attitudes and related behaviors (e.g., job satisfaction and turnover) (see Morgeson and Hurphreys, 2006), researchers are beginning to examine learning as an important outcome of these types of job characteristics. For instance, a survey of more than 800 workers between the ages of 18 and 65 from various industries found that job demands and autonomy have a positive relationship with self-reported learning at work (Raemdonck et al., 2014). Future research might consider more-objective learning outcomes such as knowledge acquired, but this initial research on worker self-perceptions is promising.
Though workplace training is important, most workplace learning is employee-directed (Tannenbaum, 1997). The employee (i.e., the learner) must manage his own work-related learning by identifying knowledge competencies and gaps, setting learning goals, monitoring progress, and adapting strategies to meet learning requirements. All of these activities are components of self-
regulated learning (Schultz and Stamov Roßnagel, 2010; Zimmerman, 2000). Though there is a significant body of work on self-regulation, little is known about how professionals regulate their own learning in the context of daily work. Most research in this area has focused on K-12 students. Moreover, for both adults and children, self-regulated learning has also typically been studied in laboratory conditions, which do not necessarily illuminate the impact of the real-world social and organizational environment on an individual’s practices (Margaryan et al., 2013).
However, some work on questions about self-regulation of learning in the workplace suggests that each workplace is a complex system, where individuals’ work and learning activities are highly influenced by the workplace community and its social norms. The workplace system and community influence the defining and evaluating of learning goals, adaptation of strategies to social and organizational norms, and the nature of incentives and hindrances to learning (e.g., Siadaty et al., 2012). The distinctive features of a learning environment can also influence whether a learner uses self-regulation practices and whether she achieves desired goals (Boekaerts and Cascallar, 2006; Siadaty et al., 2012; Whipp and Chiarelli, 2004). An example of this work is the qualitative study cited above of how knowledge workers in a multinational energy company set and attained their learning and developmental goals to complete a specific task (Margaryan et al., 2013). The researchers found that learning in this workplace was structured and deeply integrated with the work tasks and priorities and that the focus was on outcomes (short- and long-term organizational needs relating to the project) rather than process goals.
People learn continually through active engagement in their environments, and research has demonstrated that engaging in some activities promotes healthy aging, including performance in cognitive tasks (Bielak et al., 2012). The type of activity matters, however (Bielak, 2010; Carlson et al., 2012; Christensen et al., 1996). For example, an engaged lifestyle was positively associated with a reduction of older adults’ risk of cognitive impairment (Carlson et al., 2012), and the activities that had the strongest correlations were physical activities (Gow et al., 2012).
Work activities have also been shown to be important in reducing the risk of cognitive impairment, particularly when they are mentally demanding (Bosma et al., 2002). The effects of job challenges on cognitive functioning have been shown both during employment and after retirement (Fisher et al., 2014). Highly complex work with other people (e.g., mentoring and supervising functions) has been associated with increases in verbal ability in the years leading up to retirement, compared with less complex work that involved interactions with other people (Finkel et al., 2009). Declines in cognitive
performance have been observed in individuals who had higher physical or visual job demands (Potter et al., 2006).
Similarly, Potter and colleagues (2008) found that work requiring higher levels of intellectual and social effort was associated with better cognitive outcomes, whereas work requiring greater physical effort was associated with cognitive declines. This finding may seem counter to the finding that physical exercise enhances cognitive abilities (Gow et al., 2012). There has been no definitive research on the topic, but it seems likely that a balance of cognitive demands and physical exercise may preserve abilities. It may also be that physically taxing jobs may not promote the type of physical activity that is associated with enhancing cognitive abilities (e.g., aerobic versus static-strength-type exercises such as lifting) (Hertzog et al., 2008).
Although most research on activities and aging is correlational or observational in nature, some experimental research has demonstrated causal influences of activities on cognitive outcomes. For example, Stine-Morrow and colleagues (2008) found benefits for a program that involved team competition and problem solving on a reasoning ability measure. Another study found benefits of active engagement on episodic memory for older adults (Park et al., 2014).
Researchers have explored ways to foster learning across the life span. They have not identified particular educational and learning interventions for people at specific ages, but the research does suggest factors that support continued learning.
Working collaboratively with others is both a challenge and an opportunity that learners encounter in many contexts. Teams are key to planning, problem solving, and decision making in many contexts (National Research Council, 2011). The importance of collaborative problem solving to economic stability and growth is reflected in the decision of OECD to include this capacity in its 2015 survey of student skills and knowledge (OECD, 2013). Group- and project-based training and collaboration are also recognized as among key 21st century skills (Care et al., 2016; National Research Council, 2011c, 2012b). There is little research on learning in these contexts, but the research on team performance can suggest inferences about learning. For example, in team training it may be that having some team members who dominate the learning environment could be detrimental to learning outcomes.
Collaboration can allow for a more effective division of labor, and solutions developed collaboratively incorporate multiple sources of knowledge, perspectives, and experiences. However, the literature is mixed on whether the quality of solutions from a group is better than a collection of solutions from individuals working independently. On the positive side, problem-solving
solutions by a group are sometimes better than the sum of the solutions of the individual members (Aronson and Patnoe, 1997; Dillenbourg, 1996; Schwartz, 1995). Better solutions can emerge when differences of opinion, disagreements, conflicts, and other forms of social disequilibrium are explored and addressed. However, when there is chronic discord, one person overly dominates the team, some team members do not contribute adequately, or effort is wasted in irrelevant communication, the benefits of working in teams are reduced (Dillenbourg, 1996; Rosen and Rimor, 2009).
The success of a team can be threatened by an uncooperative member or a counterproductive alliance, and it can be facilitated by a strong leader who ensures that all team members are contributing. Studies have shown that skilled collaboration and social communication facilitate productivity in the general workplace (Klein et al., 2006; Salas et al., 2008) and, more specifically, in engineering and software development work (Sonnentag and Lange, 2002), in mission control in aviation (Fiore et al., 2014), and in interdisciplinary research among scientists (Nash et al., 2003).
People benefit from training in when and how best to apply collaboration skills (Care et al., 2016; Mullins et al., 2011). For example, the ground rules of the collaborative situation must be understood by the group members if they are to optimize their interactions and solutions. Students need to know when, why, and which aspects of collaboration are fruitful for improving the knowledge to be acquired and the problem to be solved. When is it best to focus on disagreements? When is it better to negotiate a consensus? How can the group find common ground on task goals and team organization? What tasks are best conducted individually versus with a tightly coordinated team? For highly interdependent tasks (i.e., those that are impossible to achieve individually), what is the schedule and communication protocol for initiating actions and completing objectives? How are tasks distributed among group members in the team organization? How are potential problems monitored and repaired?
Research on training for these critical teamwork skills is just beginning. One framework, the Collaborative Problem Solving framework used by the Programme for International Student Assessment, identifies three core collaborative competencies: (1) establishing and maintaining a shared understanding, (2) taking appropriate action to solve the problem, and (3) establishing and maintaining team organization (OECD, 2013). These competences are crossed with problem-solving competencies: exploring and understanding; formulating a representation of the problem; planning and executing the plan; and monitoring and reflecting on the problem solution. A 2015 computer-based assessment of the program could be the basis for improvements in curricula in this area (see OECD, 2015).
Individuals continue to learn throughout their lives, but once they complete the compulsory portion of their education, what and how much they learn is largely directed by their own choices and circumstances. Both reasoning and knowledge increase up to early adulthood, when their paths begin to diverge: abilities to quickly generate, transform, and manipulate factual information begin to decline, while knowledge levels remain stable or increase. We note that because conducting either randomized controlled trials or quasi-experimental research on the effectiveness of training interventions in environments outside of K-12 settings is difficult, the research does not yet support strong conclusions about interventions. However, we offer two broad conclusions about lifelong learning.
CONCLUSION 9-1: People continue to learn and grow throughout the life span, and their choices, motivation, and capacity for self-regulation, as well as their circumstances, influence how much and how well they learn and transfer their learning to new situations.
CONCLUSION 9-2: People learn continually through active engagement across many settings in their environments; learning that occurs outside of compulsory educational environments is a function of the learner’s motivation, interests, and opportunities. Engagement with work (especially complex work that involves both intellectual and social demands), social engagement, physical exercise, and adequate sleep are all associated with lifelong learning and healthy aging.