Models and Stages

Across the literature, there are many adult development models that use stages to describe the journey of adulthood. These models provide a variety of conceptualizations to chart the course of adulthood from early adulthood, to mid, later, and even an age of wisdom. Models include many different aspects of development, such as psychosocial, cognitive, social, physical, and so on (Bee, 2000; Bee & Bjorklund, 2008; Bjorklund, 2014; Lemme, 2002).

In the dominant models of human development, some will recognize specific trigger events, and other models will be more dependent on chronological advancement. Notable models that contribute to understanding adult learners' adoption and learning of technology include those related to psychosocial and cognitive development.

Erikson's (1950) identity development stages have had a significant contribution to the field of psychology and child-rearing. Over the course of his work he provided a much broader understanding of adult development than Piaget's (1951) cognitive development model, which focused almost exclusively on child development. Table 4.1 displays the stages of Erikson's model.

Source: Adapted from Erikson (1950).

A late collaborative publication by Erikson, Erikson, and Kivnick (1986) recognized continued intellectual and social development beyond childhood and youth. This book focused on the important contributions that older adults make to society.

However, other researchers were also exploring the topic. In the 1980s Vivian Clayton (1982) as well as her coauthor James Birren (Clayton & Birren, 1980) explored a familiar pathway—the philosophical roots and beliefs related to wisdom. These studies and explorations remained inconclusive in part because of design issues. Also during the 1980s, Baltes, Smith, Staudinger, and Kunsmann formed the Berlin Wisdom Project in Berlin, Germany at the Max Planck Institute for Human Development in order to begin researching wisdom through scientific studies (Baltes, 2004; Baltes, Dittmann-Kohli, & Dixon, 1984). The research of Baltes, the lead author, had originally been in the area of life span development.

Given the advancement of andragogy and models in the margins of educational policy and practice, the recognition of adult's continued development would eventually grow in significance (Hall, 2011). Understanding research on wisdom requires discussion across its many layers; however, it unfolds an abundance of opportunities to work with adults in their later years. For instance, Erikson originally identified this stage as 65+. However, the average US life span continues to lengthen and currently is 81 and 76 years for women and men, respectively, in the United States. Therefore, how will these additional decades, a good portion of adults' lives, be spent in this stage of wisdom (Organisation for Economic Co-operation and Development [OECD], 2013)? In addition, what do mature adults seek for continuing learning?

Vital questions then arise, such as how do instructors and program developers use technology to afford more opportunities for older adults to continue to grow intellectually and contribute to society? With opportunities for online communities and a global distribution of information, there should be fewer constraints related to time, physical space, or mobility in regard to learning.

Cognitive Development Theories

The adult learning literature has several major cognitive development theories that have different foci and pathways, such as Dewey's experiential learning (1916); Piaget's stages (1972); King and Kitchener's reflective judgment (1994); Perry's schemes (1970); and Belenky, Clinchy, Goldberger, and Tarule's women's ways of knowing (1986).

This section briefly describes, compares, and contrasts these theories. Educational scholars generally agree that the first discussion of experiential learning occurred about 250 bce by the popular and notable scholar Aristotle. He stated that people needed to learn some things by doing them. Because of these early roots, experiential learning has been one of the most widespread and enduring theories of learning. Over time, experiential learning has also been differentiated into specific types.

In 1916, despite the dominant understanding and practice of his time of recitation (repeating memorized lessons) instruction Dewey was the first to articulate experiential learning theory in-depth. His model incorporated what he had observed in his work. Dewey recognized that learners gained drastic improvement in understanding and skills when engaged in hands-on learning compared to more passive approaches (Miettinen, 2000).

Dewey also had a model of reflection (see Figure 4.1) that connects thought and action. Reviewing this model greatly aids in understanding Dewey's concept of experiential learning (Miettinen, 2000), especially the critical role of reflection. The figure illustrates a cyclical and potentially repetitive process. The stages alternate among hypothesis, testing, revising based on data, and if, necessary, testing the revised hypothesis. The experiential learning model incorporates the steps and dynamic actions of working through perception, proposal, action, and adjustment.

Beginning with his research in the 1970s, Kolb’s (1976, 1984) work on experiential learning popularized the theory with a four-stage model. Since then, Kolb's work has been extensively used across educational contexts in the United States and internationally for instructional design, teacher education, faculty development, and innovation. At the same time, it is important to note that Kolb's work may not have perfectly captured Dewey's intent. Some authors have criticized the theoretical reasoning applied by Kolb in this key publication and make a strong case for more focus on the role of thought in experiential learning (Miettinen, 2000).

Piaget's stages (1972) are also foundational in the development of Kolb's model of experiential learning. Piaget's four stages of cognitive development for children and adults illuminate how reasoning develops over time:

1. Sensorimotor: Birth through ages 18 to 24 months
2. Preoperational: Toddlerhood (18 to 24 months) through early childhood (age 7)
3. Concrete operational: Ages 7 to 12 years old
4. Formal operational: Adolescence through adulthood

When Kolb used Piaget's stages in the development of his experiential learning theory, he approached the model from a cognitive orientation and a sequential step model. Because Piaget's model had been widely tested, accepted, and adjusted over the ages, it provided a strong cognitive orientation for experiential learning.

King and Kitchener's (1994) model of reflective judgment illustrates another dimension of the field's understanding of adult learning. Similar to Kolb's theory of experiential learning, the reflective judgment model built on many relevant theories to explain learning experiences. Just as Dewey recognized the critical role of reasoning in problem-solving, the model of reflective judgment described the changes that occurs in how people think. Specifically, King and Kitchener (2002) documented their understanding of how adults' knowledge (their epistemic assumptions) and beliefs interact through the development of critical or reflective thinking skills. King and Kitchener's model has seven stages that are grouped in three levels of thinking: pre-reflective (Stages 1–3), quasi-reflective (Stages 4 and 5), and reflective (Stages 6 and 7).

In the same vein as the model of reflective judgment, Perry (1970) proposed his scheme of intellectual and ethical development. It had nine positions that described the changes that may occur in the thinking of adults, specifically those enrolled in college. Perry's work provided a taxonomy to understand how learners shifted from dualistic thinking to more complex and integrative understandings of knowledge and beliefs. One problem that has emerged with Perry's model is the assumption that changes in personal ways of knowing (epistemology) develop only in prescribed, fixed series of stages (Schommer, 1990). With greater understanding of diverse populations and cultures, researchers and educators recognize that a more dynamic model would be more descriptive of human experience (Belenky et al., 1986; Gilligan, 1982).

Although the schemes might sound similar to skills involved in critical thinking, there are important differences. As Bruning (1994) pointed out, Perry's schemes refer to the process of metacognition in which individuals examine their own reasoning processes. Beyond the foundational tests inherent to critical thinking, which evaluate facts, arguments, and constructs, metacognition determines the appropriateness and effectiveness of the reasoning approach, logic, and process. This powerful focus on metacognition, or “how we know, what we know,” is one of the reasons that, despite its limitations, Perry's schemes continue to have a significant place in the literature and practice. His model has been a cornerstone for additional adult and higher education research, teaching, and student development services (e.g., Baxter Magolda, 1999a, 1999b, 2014; Belenky et al., 1986, etc.).

Belenky et al.'s (1986) women's ways of knowing was established based on extensive research with a cross-section of women in the 1980s. This research of women adults revealed an overlap in stages with Perry's model but also unique cognitive stages by gender.

In Belenky et al.'s (1986) model, there are five “ways of knowing” that are also aptly described as “knowledge perspectives.” Each way of knowing identifies a unique, descriptive stage in adult women's cognitive development, which reveals how and whether their way of knowing is dependent on conceptions of self (self), relationship with others (voice), or their understanding of the origins and identity of critical concepts such as truth, authority, and knowledge (Belenky et al., 1986).

Figure 4.2 illustrates the five major stages of the women's ways of knowing model and the sequence in which cognitive development generally flows. Major differences in this model for women's cognitive development versus Perry's scheme are evident immediately in several areas: (1) an initial stage of silence, (2) knowing as perceived in community (connected) or individual (separate) frames, and (3) origins of truth (received, subjective, procedural, or constructed). This model, as well as the work of Gilligan (1982) on moral development, demonstrate how the 1980s were a critical time of psychological and educational research more fully investigating gender differences.

Kegan's (1994) levels and orders of consciousness provide another perspective of understanding how cognitive development addresses the many demands and conflicts of our complex lives in the digital age. Kegan's is described as a constructive-developmental theory because it describes learners' constructions of reality and the development of those constructions into specific, more complex levels. As seen in Table 4.2, the model has five orders of mind, ranging from a 2-year-old through middle adulthood. Each of these successive orders includes important advances in meaning-making and complexity. Foundational points to notice about Kegan's orders include (1) individuals build on (scaffold) the previous order and yet transform (their understanding of the world changes), (2) the fact that the orders are not intrinsically ranked in value, and (3) it is most important to align the order of mind and the task required.

Related to these foundational points are two critical issues when working with Kegan's orders: the relative value or worth of each of Kegan's orders and the people functioning at each level. Because Kegan's work has been researched further and evolved into applications to working with undergraduate students, student affairs, and coaching (Ignelzi, 2000; King & Baxter Magolda, 2005; Love & Guthrie, 1999), it is important to clarify these points. No one can make an assumption that a person in any of the orders will act in a manner that is responsible, ethical, caring, and so on. The order of the mind in which a person functions is not an indication of that person's value, worth, or ethics. The order of mind solely describes how an individual thinks and works through cognitive processes.

Kegan (1994) illustrates the practical application of the orders to teaching when he introduced “building a consciousness bridge” (p. 278) to assist students in moving from order four to order five. Such approaches guide educators to understand the needs of learners and develop appropriate instructional strategies for facilitating development across the orders.

Jonassen's (1994) work shifts the focus of cognitive development to the specific use of technology as tools. His model includes viewing technology as providing mind tools (or strategies) that learners can develop and then apply, expand, transform, and use in many different contexts. Jonassen's roots in constructivism help him focus on critical thinking but still bring problem-based learning into the forefront (Jonassen, 1994; Jonassen, Howland, Moore, & Marra, 2003). Several others have continued to use constructivism in online learning research (Doering & Veletsianos, 2008; Huang, 2002; Ongito, 2013). The focus in these models is on cognitive development, not moral reasoning or epistemologies as addressed in the other discussed models. Constructivism provides a valuable framework for learner-centered instruction and active learning. Instead of learners passively watching or listening to content, constructivism focuses on experiences of discovery and new understandings.

There is richness in having a constellation of choices of intellectual and moral development models; however, the wealth simultaneously creates value and difficulty because of the challenges of demarcating these approaches and theories. In part, the mystery is embedded in the origins and details: the context and goals may be dissimilar. Several of the theories, such as Belenky et al.'s (1986) and Dewey's experiential learning (1916), had roots in radical perspectives to comprehend and validate different ways of learning. Others were more theoretically focused in seeking to make sense of meaning-making in complex contexts (Kegan's [1994] levels and orders of consciousness, Piaget's [1972] stages, and Jonassen's [1994] mind tools).

Intrinsic Motivation

Models of motivation and learning also abound in discussions of adult learning because, in comparison to younger learners, adults often have the powerful asset of greater intrinsic motivation for learning what they pursue (Knowles, 1968; Mahan & Stein, 2014; Merriam & Bierema, 2014). Certainly, there are situations in which adults are mandated to engage in learning, and the literature confirms that such arrangements create enormous barriers for meaningful learning (Rockhill, 1983). Whether adults are required to complete professional development hours on a regular basis or attend court-mandated driver education classes the fact that they do not have a choice of whether or not to engage in the educational program creates obstacles immediately. By contrast, adults choose to engage in learning in many ways throughout their daily personal and professional lives.

Neurobiology further proves that when motivation is engaged, learning is more efficient and effective (Mahan & Stein, 2014). Specifically addressing Western culture in the 21st century, Pink (2009) describes intrinsic motivation as a powerful driver because of several cultural and economic shifts. These shifts are in a large part because of the radical transformation of work from being less intellectually stimulating repetition (e.g., assembly lines, factory, etc.) to more creative, self-directed, and team-based efforts (Merriam & Bierema, 2014; Pink, 2009).

The two aspects of 21st-century motivation that Pink (2009) identified as having emerged from this shift in the nature of work are autonomy and mastery. Adults who are employed in autonomous-oriented roles are able to be self-directed, independent, and explore new opportunities and creative strategies for accomplishing their work-assigned needs.

The think tanks of the 1980s and 1990s may have been among the first popularization of these concepts in our culture, but the approach has always existed in higher education among the science, technology, engineering, and mathematics (STEM) fields especially. However, in the digital age, widespread technology innovation has been adopted and integrated into our society and work and so has the nature and demand for more creative work-related learning. When the locus of control shifts to an individual or small group, change is able to occur much more swiftly than when it has to be approved, planned, and managed through higher levels. In essence, autonomy enables employees to be more responsive to the changing conditions and demands that affect their work.

However, autonomy has pros and cons. As a double-edged sword, autonomy has the additional requisite of greater responsibility and self-regulation and self-control. Autonomous employees must demonstrate and sustain the ability to stay on schedule and produce their assigned outcomes and goals. Simultaneously, employees benefit greatly from autonomy immediately because their sense of pride and self-worth increases with greater responsibility. Over the long term, as employees achieve their goals, self-esteem can surge even further. These changes may result in greater aspirations overall. In situations of risk, some degree of autonomy can unleash greater achievements than using mandates exclusively.

One can begin to recognize how Pink's (2009) 21st-century mastery characteristic is closely aligned with autonomy. As adults in the workplace become successful in more autonomous learning, their self-confidence and aspirations increase, which leads to another new instance of seeking mastery. Rather than satisfying the status quo, or accomplishing the least learning possible to meet the requirements, mastery thrives on seeking more learning and chasing excellence.

Key characteristics of the creative minds of some of the great artists, musicians, and scientists include persistence, endurance, hunger for learning, and thirst for new solutions. In many of them there was an insatiable desire to discover new knowledge or perspectives. History is full of accounts of sleepless nights, sacrifice, and unyielding efforts to master the conquest at hand.

Given the growing scope of innovation and spread of technology integration, the digital age will continue to provide extraordinary demands for intrinsic motivation (autonomy and mastery) in learning. Therefore, the following strategies are recommended to address this need. As learners and educators we need to (1) seek and design learning opportunities consistent with real-world demands for autonomy and mastery; (2) find opportunities to become confident in self-directed, autonomous learning; (3) seek opportunities to cultivate and tap intrinsic motivation in preparation for work and daily life situations; and (4) develop more authentic and real-life assessments to evaluate learning.

Learning Preferences

Although it has dominated the literature and much of the research in the adult learning field for decades, learning style models have also been under fire. Kolb (1984) formalized the first widely recognized models of learning styles and provided an accompanying inventory. The model found wide acceptance among educators and learners because it resonated with their experience of different learning approaches being more or less effective depending on the individual.

The objections to the learning style model has mostly arisen from researchers and scholars who accurately identify the lack of research support for the details of the theory (Paschler, McDaniel, Rohrer, & Bjork, 2010). Several researchers and educators have pointed out that people shift their learning styles based on the context or content. Moreover, it has been confirmed that many learners use several learning styles either simultaneously or at different times. People are not boxed in to using only one learning style all the time (Khaldi, 2016; Peterson, DeCato, & Kolb, 2015).

Finally, the widely used and revered learning style instruments have been questioned many times regarding their reliability and validity. Yet the model, or myth as Willingham, Hughes, and Dobolyi (2015) refer to it, persists (Bee, 2000; Merriam & Beirema, 2014). Indeed, there is a better choice that incorporates the accurate elements of the learning style model and overcomes its limitations: learning preferences.

Learning preferences embrace several of the critiques related to learning styles (Mahan & Stein, 2014; Willingham et al., 2015) and captures the valuable perception by adult learners that some learning strategies work better for them than others. At the same time, learning preferences allow for flexibility in learning strategy choices based on other factors. For instance, one of the major challenges faced by the learning style model was its lack of integration of context. The concept and mental model of learning preferences inherently includes the ability of learners to select their favorite method based on the circumstances, content, available resources, and so on.

Research by Khaldi (2016) confirmed the findings of others that accessing multiple learning styles were in fact the most effective way for their adult participants to advance in their attainment of new knowledge and skills as well as reach their goals. Moreover, in recent research publications, Kolb has agreed that flexibility needs to be incorporated in the concept and use of learning styles (Peterson et al., 2015).

The digital age affords an abundance of opportunities for learners to access their learning preference based on their specific needs. On-demand technologies are one of the most significant developments in this area, because they enable learners to access or download learning support content (videos, audios, demonstrations, lectures, slides, tutorials, articles, etc.) whenever they have online or mobile access. Building the following skill sets among adult learners enables them to use their learning preferences: (1) develop awareness that different learning preferences exist, (2) introduce potential sources and types of available learning resources, (3) explore how to access the resources and develop skills to learn new access sources as they emerge, and (4) cultivate strategies to plan and prioritize the many learning sources and opportunities to prevent being overwhelmed.

Neuroplasticity

Unequivocally, this section will prove that in the digital age thinking is no longer the same. Major contributions to knowledge of adult cognition have emerged from the field of neuroscience. Neuroscience, also known as neural sciences, is the study of the structure or function of the nervous system and brain. Recently, neuroscience research has been widely advanced because of timely innovations in medical imaging that enable detailed study of the brain's physical structure and activity. Functional magnetic resonance imaging (fMRI) became a powerful tool for studying the cognitive function of the human brain during the 1990s (Lemme, 2002). Although another imaging technique, positron emission tomography (PET), had been available previously, PET facilities were not readily available and expensive to build. With the ability to use the local and more plentiful fMRI facilities to study cognitive processes, the field of neuroscience and the study of adult cognition began to surge ahead.

This development occurred so rapidly that even a few decades after these tremendous advances, widespread false beliefs about adult cognition continue to persist among the general public. For example, even though it is incorrect, most adults, who have not been trained in neuroscience or adult learning, still believe that once humans reach adulthood their mental capacity has reached its maximum potential and only decrease (Begley, 2007a). Popular phrases we hear daily illustrate this deep-seated cultural belief about aging and learning: “Old dogs can't learn new tricks,” “College is for young people,” “I'm too old to learn a new career,” “I'm too old to go to college,” and “My time is past; the career train left the station a long time ago.” Neuroscience discoveries prove that all those popular reasons not to learn may be excuses, but they are not facts. In addition, neuroscience continues to fuel research in adult learning for the digital age (Johnson & Taylor, 2006).

As early as 1999, Gould, Tanapat, Hastings, and Shors reexamined evidence regarding the growth of neurons during learning tasks among mammals, including adults. Their conclusions were that although further research was needed, learning resulted in new neurons being developed in the hippocampal region of the brain. With this new information, determining the nature and relationships of the new neurons in learning was on the horizon.

In fact, in 2010 several studies were published that demonstrated from different directions that adults' brains continue to add new neuropathways. Taubert et al.'s (2010) research demonstrated that as adults learn, their brains continue to grow, expand, and develop new neural networks. Schlegel, Rudelson, and Tse (2012) further investigated neural network development among college students engaged in language learning. Using diffusion tenor imaging (DTI) this research documented that the adult brain continues to change by producing specific effects on the myelin sheath of neurons (nerve cells) (Schlegel et al., 2012). Moreover, these changes in the neurons contribute to the reshaping of the brain.

Schlegel et al.'s (2012) article profoundly synthesizes and articulates the body of research regarding the neuroscience of adult learners: “Our data support the emerging view that the adult brain retains a robust capacity for reorganization with learning. Like a muscle that grows with use, the brain appears capable of expanding the functionality of networks involved in learning by altering the underlying anatomy through myelination” [emphasis added] (Schlegel et al., 2012, p. 1669).

The principle of neuroplasticity was developed by many philosophers, educators, and scientists over centuries. There has been a rush of research studies and results since the late 1990s that have closed the gap of understanding and have begun to widen the vista of even greater possibilities for neuroscience. These many studies agree that given different conditions, different parts of the brain have the capability of reshaping itself based on the learning context and needs encountered (Begley, 2007a; Doidge, 2007). Not only does the brain continue to lay down new patterns and morph into new shapes, strengths, and abilities but also one does not have to be actively engaged in motor skills for these changes to occur. Much of the initial application of neuroplasticity has been in medical science. Researchers have used the principles to develop tools and strategies for adults with disabilities, stroke victims, and so on to remap their brain and regain essential abilities (Doidge, 2007; Raskin, 2011).

However, there are also great possibilities for the field of education. In several studies conducted among different constituencies (college students, mental health patients, Buddhist monks, etc.), it has become apparent that metacognition (thinking about thinking) can effect change in the brain's neuropathways (Begley, 2007a; Bush, 2011). In essence, it would appear that our mind and concentration are so powerful that mental ability (thinking) has the power to physically change our brain.

Certainly, these new areas of development in neuroscience provide expansive landscapes of possibilities for technology innovation that will leverage the mind in ways never previously imagined (Barbezat & Bush, 2014; Bush, 2011). Educators of adults need to stay up-to-date with these advances in order to shape future technology use, instructional design, programs, and policies to benefit the needs and goals of learners.

More Complex Thinking and Learning

Another model that has anchored learning theory and practice for decades is Bloom's taxonomy (1950). As seen in Figure 4.3, this model provides a hierarchy of familiar cognitive strategies that has basic skills of comprehension as the foundation and critical thinking, analysis, and interpretation much higher on the level of complexity. Similar to many others, this model includes the understanding that not all learners might reach all stages but that as educators we work with them to scaffold their fundamental skills to higher-order thinking levels. Furthermore, adult learners should participate in choosing the goals for their education and study. This means that instructors need to serve as facilitators for learners to reach their goals, not determinants of the end game or pathway.

The understanding that adults are generally capable of more complex thinking and learning has emerged not only from educational psychology and experimental psychology but also from neuroscience. Researchers examined adults' brains with digital imaging and discovered that adults have more development in both hemispheres than youngsters. Additional evidence confirmed that when engaged in solving problems of learning, adults used both sides of the brain and not just a single hemisphere (Begley, 2007a; Doidge, 2007; Mahan & Stein, 2014).

Bloom (1956) also proposed three domains of learning (cognitive, psychomotor, and affective learning), which are also related to three different processes (intellectual, physical, and emotional). The research from neurobiologists confirmed this research when they found that when more than one domain was involved in learning, learning lasted longer and was more extensive and powerful. Furthermore, the learning was most effective when it included all three domains (Mahan & Stein, 2014).

These findings complement what educators know about active, experiential, and kinesthetic learning. When adults use several learning domains to tackle new content or problem-solving tasks, they have more opportunities to discovery nuanced insights through multiple cognitive and sensory modes (Kolb, 1984). In addition, andragogy stated that adults will scaffold connections from prior experiences to create new understandings (Dachner & Polin, 2015; Knowles, 1968, 1975). Now, neuroscience has further explained the biology behind these phenomenon.

Critical Connections with Prior Experiences

Andragogical (Knowles, 1968, 1975) principles recognize that adult learners benefit from connecting prior experience as points of critical connection for new learning. Dewey (1916, 1938) had been exploring aspects of this principle in vital ways many decades earlier as he worked with adults in vocational settings. Fundamentally, experiential learning has the learner at the center of action, often in the context in which the new knowledge needs to be applied (formally known as in situ). Their experience acts similar to immersion in the principles, concepts, and actions of study. It provides a laboratory for discovering new understanding. This approach is in stark contrast to traditional learning models in which people study the theory and research of a topic prior to hands-on experience. Experiential learning radically reverses the order and agent in the learning process. This simple explanation clearly illustrates the roots of experiential learning in constructivist philosophies of education that focus on student discovery and construction of new understandings and knowledge versus transmission of knowledge from an expert (Jonassen, 1994).

Of course, one cannot separate learners' actions in an experiential learning setting from their prior knowledge. Learners' actions and choices are based on how much they know about an area and their prior skills and knowledge. For example, if learners understand the basics of how to operate a GPS, they can build on them to advance their geography skills by exploring and differentiating among different longitudes and latitudes via GPS applications for a smartphone. However, if they do not yet comprehend what longitudes and latitudes are and how they work, then those skills first need to be the focus of their learning experiences.

Applying prior learning to the same example, if we connected the longitudes and latitudes skills to something familiar in the learners' background, they would learn more quickly and it would be more likely to be stored permanently in their memory (Mahan & Stein, 2014). In this example, instructors could use road maps and nautical maps to illustrate how longitude and latitude are used to identify geographical locations. Once these concepts are understood and applied in tangible, practical ways, the pathway for understanding electronic GPS is easier to traverse.

Experiential learning was further advanced with Kolb's (1984) description of the four different abilities used when engaged in concrete experience, reflective observation, abstract conceptualization, and active experimentation. However, there are many ways and factors to think about how experience and learning connect in the lives of adult learners. Some of these factors are contributions of sociocultural contexts (Tennant & Pogson, 1995), psychoanalytic contexts (Fenwick, 2003), prior learning connections (Tennant, 2012), and reflection and non-reflection (Jarvis, 2006; Schön, 1983). As learners and educators, we have opportunities to select those factors that may best assist learning efforts.

Clearly, scaffolding learning by connecting to prior experience and understanding has many benefits for adult learners. Educators may leverage the principles of experiential learning by connecting prior knowledge when teaching technology skills or guiding adults through the many changes that technology innovation creates. Such approaches build on research and theory to provide effective instructional strategies.

The value in this activity includes the fact that although the characters and stories in the movies might be fictional, they still resonate with viewers because of common experiences and empathy for the journey. Another great benefit of the activity is that your analysis will be of a person with whom you have no relationship. This situation often provides greater clarity of insight and freedom for discussing the issues recognized.

Conclusion

The great news discussed in this chapter is that many popular beliefs about declining mental abilities in adulthood have been proven false! The expanse of adulthood includes more years than ever before. Furthermore, science has helped demonstrate that adults can continue to be active learners during their mature years. These advancements result in many opportunities for educators to design new instructional programs, initiatives, and strategies that may appeal and serve adults of all ages.

In addition, the nature of work in the digital age has changed to be less mundane, more creative, and more autonomous. Such changes mean that educators need to prepare self-directed learners in order to be successful in these very different environments. Based on the emphasis for more self-discipline, management, and creative opportunities, those employees who will be most successful will be more autonomous and intrinsically motivated to seek and master excellence.

The scientific and technology advancements of the digital age are truly profound; however, the related challenges in our daily and work lives are also great. The digital age has created an urgent need to understand and facilitate adult learning skills.