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Preparing for Disruption by Creating Future Possible Selves
It is perhaps more evident now than at any other time in human history that current technologies are racing ahead while our skills and organizations are lagging behind. In their seminal book Race against the Machine, Erik Brynjolfsson and Andrew McAfee, both economists at the Massachusetts Institute of Technology, confirm the global convergence of technological, social, and economic trends and make the case that the trajectory of digital technology is transforming labor markets in unpredictable ways.1 This pronounced restructuring of labor markets—what Brynjolfsson and McAfee term “the Great Restructuring”—is placing new and complex demands on education and on those connected with the educational enterprise, including students.
In the context of such uncertainty, Maxine Greene’s and Theodore Sizer’s ideas about empowering students to develop resiliency, perspective, judgment, and flexibility, as well as our responsibility to nurture their creativity and imagination, have never mattered more.2 Students’ acquisition of these habits of mind hinges in part on their ability to develop positive and enduring social capital and networks; emotional intelligence; and an agentic perspective, or the belief that they are capable of authoring their own futures.3 Students also need to nurture the skills, abilities, and dispositions necessary to regulate a range of emotions in myriad social contexts.
These characteristics and capacities are critical as students face new challenges associated with a quickly evolving workforce, including the challenge of working creatively with, rather than being limited by, new technologies. As former president Obama emphasized in his farewell address to the nation, “the next wave of economic dislocations won’t come from overseas,” but “from the relentless pace of automation that makes a lot of good, middle-class jobs obsolete.”4 It is thus imperative that higher education institutions reimagine student prospects for upward mobility and professional success by helping students to cultivate a creative mind-set and the mental elasticity, courage, foresight and skill, to invent, discover, and create in ways that respect the fragile and finite resources of our world.
Economists’ projections of change
Economists Daron Acemoglu and Pascual Restrepo recently examined the effects of automation on employment and wages in manufacturing and found that the use of industrial robots in the automotive and electronics industries in local labor markets in the United States contributed to the loss of 360,000 to 670,000 manufacturing jobs between 1990 and 2007. Acemoglu and Restrepo estimate that for every one robot per one thousand workers, approximately six workers lost their jobs and wages declined by nearly 0.34 percent. They suggest, moreover, that the number of jobs lost in manufacturing is likely to increase as a result of automation, particularly given projections by the Boston Consulting Group that by 2025, one quarter of industrial manufacturing tasks worldwide will be performed by robots.5 Notably, Acemoglu and Restrepo were surprised when they did not find “positive and offsetting employment gains in any occupation or education groups” to counteract the jobs lost in manufacturing.6 As described in a recent New York Times article, Acemoglu and Restrepo suggest that while an increase in employment could still occur, “there are large numbers of people out of work, with no clear path forward—especially blue-collar men without college degrees.”7
The effects of automation in the workforce are not limited to manufacturing, however. The authors of a January 2017 report from the McKinsey Global Institute assert that “advances in robotics, artificial intelligence and machine learning . . . match or outperform human performance in a range of work activities, including . . . cognitive capabilities.” McKinsey researchers used data from the United States Department of Labor to estimate the automation potential of more than two thousand work activities in more than eight hundred occupations across the economy, with the activities organized in five categories: sensory perception, cognitive capabilities, natural language processing, social and emotional capabilities, and physical capabilities. In their analysis of automation potential and adoption timing scenarios for the United States and forty-five other national economies representing about 80 percent of the global workforce, McKinsey found that approximately half of current activities in the global economy, corresponding to nearly $15 trillion in wages, have the potential to be automated by adapting currently available technology. Automation is not a new phenomenon, however, nor is disruption in the workforce: for example, agricultural employment in the United States “fell from 40 percent in 1900 to 2 percent in 2000,” and employment in manufacturing declined “from approximately 25 percent in 1950 to less than 10 percent in 2010.” Nonetheless, automation will precipitate “significant labor displacement and could exacerbate a growing skills and employment gap” between high- and low-skill workers.8
Other economists draw different inferences from the pace and scale of technological innovation. David H. Autor, for instance, posits that technological innovations may displace jobs from one part of the economy to another and/or may complement, rather than replace, humans.9 Meanwhile, Carl Benedikt Frey and Michael Osborne, who analyzed real-world data on 702 job categories, found that 47 percent were susceptible to automation within the next twenty years. Frey and Osborne, however, were more optimistic than Acemoglu and Restrepo that jobs lost would return.10
Given these projections, and Claudia Goldin and Lawrence F. Katz’s finding that low levels of human capital development have been the norm in the United States since 1975,11 it is imperative that American colleges and universities commence or continue dialogue about the new reality of work. This includes addressing the notion that as learning machines become more “cognitively” sophisticated, and the gap between machine and human abilities diminishes, those charged with hiring will increasingly have to decide whether to “hire” machines or people.12
The skills students need
What does the rapidly changing landscape mean for our students? In the Great Restructuring, according to Brynjolfsson and McAfee, a disproportionate share of economic rewards will accrue to “high-skill workers,” “superstars,” and “owners.” The authors define high-skill workers as those with the ability to engage in abstract and data-driven reasoning driven by the demands of data visualization, analytics, high-speed communications, and rapid prototyping. Moreover, advances in high-speed communications and technologies that encourage collaboration have increased employers’ access to a global talent marketplace in a growing number of fields, including programming, marketing, design, writing, and consulting. More companies are thus open to outsourcing key roles to superstars who will flourish while the local talent pool becomes under- or unemployed. And as Karl Marx made clear nearly one hundred and fifty years ago, those with capital and those who own or control the means of production (owners) will thrive. At this juncture in human history, current trends in technological innovation suggest a declining need for human labor in many industries; consequently, the proportion of wealth returned to those who invest capital or who own the technology we use is growing exponentially.13
Cal Newport identifies two core abilities for success in the new economy: (1) the ability to quickly grasp difficult tasks or ideas, and (2) “the ability to produce at an elite level, in terms of both quality and speed.” These core abilities also apply broadly to fields that have seemingly little to do with technology. Newport posits that the capacity to quickly master hard things and to produce at elite levels depend on our adeptness at performing deep work, which he defines as the ability to focus, without distraction, on cognitively demanding tasks.14 Essentially, deep work requires the ability to consistently plan for and engage in the deliberate practice of particular skills or abilities, and using feedback to constantly improve. These properties of deliberate practice suggest the importance of uninterrupted concentration, which K. Anders Ericsson reminds us is antithetical to diffused attention.15
The identification of deliberate practice as a valuable behavior has contributed to recent research on the neurophysiological mechanisms that inform progress on difficult tasks. This work points to the role of myelin, a layer of fatty tissue that grows around neurons and functions like an insulator that enables the cells to fire faster and cleaner. We become better at a skill when we develop more myelin around the relevant neurons, which in turn enables the corresponding circuit to fire more effortlessly and effectively.16 However, if we are learning or practicing a difficult skill in a distracted state, we are, in effect, firing too many circuits concurrently and randomly to isolate the group of neurons we want to strengthen. Current scientific findings are thus expanding our understanding of the importance of focusing deeply, without distraction, when trying to quickly learn difficult skills and concepts. Regardless of the field in which students eventually work, the capacity to develop these abilities, and to exercise them in an agentic way, may be defining attributes of those who are successful in the new economy.
Education for the new economy
What does the increasing prevalence of driverless cars powered by artificial intelligence, as well as robots that can interpret medical images, evaluate vast amounts of data, conduct legal research, advise oncologists, and analyze stocks, mean for our students? How are we, as architects of the higher education experience, preparing students for this new reality? How can we enable students to develop the fundamental transferable skills, abilities, and dispositions that they will need to work well and creatively with technology to drive innovation, rather than be replaced by it?
The National Research Council advocates for the establishment of environments in which students can develop not only the ability to master difficult concepts, but also learn how to transfer those skills and abilities to new situations.17 These environments—including those that are learner centered, knowledge centered, and community centered—can help students to organize their knowledge and personal experiences into demonstrable results that they and others value.18
In institutions of higher education that are student centered, faculty pay close attention to the knowledge, skills, abilities, and dispositions that students bring into the classroom and other learning venues. This awareness enables faculty to address students’ preconceptions about the subject matter at hand while simultaneously promoting shifts in students’ thinking, especially if students’ perceptions are not entirely accurate. Additionally, intentional attention to the background knowledge that a student brings into a new learning situation can reveal students’ conceptions of what it means to be intelligent. Indeed, students who believe that intelligence is a fixed entity are more likely to be performance oriented as opposed to learning oriented; they want to look good rather than risk making mistakes while learning and are thus more likely to give up when tasks become difficult. Alternatively, students who believe that intelligence is malleable are more willing to struggle with challenging tasks and to pursue them to completion.19
Faculty in learner-centered classrooms are more likely to develop appropriate tasks that facilitate deeper understanding of the material taught, and tend to be more attentive to students’ progress in moving toward systematic ways of engaging with material. In student-centered learning spaces, faculty present materials that are demanding enough to maintain engagement but not so difficult as to lead to discouragement. The noted developmental psychologist Lev Vygotsky has termed this the “zone of proximal development.”20
Faculty can also encourage students to schedule, in methodical and intentional ways, uninterrupted periods of time in which to attend to difficult but important work—a technique that Newport connects with producing at an elite level.21 Recent studies affirm that the most successful students organize their time to enhance their concentration, which dramatically reduces the time it takes to master challenging material without diminishing the quality of results.22 Developing this ability reduces what Sophie Leroy calls the attention residue effect, which occurs when shifting from task to task.23 That is, when our attention is divided, we are unable to fully concentrate on the task at hand because a part of our attention is still focused on other tasks.
The idea of the malleability of intelligence can inform how we structure social and pedagogical interventions to reduce performance gaps among students. It also encourages us to recognize the diverse ways in which students can be engaged and the importance of reinforcing positive messages that can help students counter the many and varied odds against them. Recent research can help us to reorient our own perceptions with a view toward integrating new and nuanced understandings of existing programs and interventions and to strive for outcomes that include motivating an increasingly diverse student population to consider a wider range of future “possible selves.”24 When implemented well, this strategy results in more positive campus climates that enable students to immerse themselves more deeply in learning.
The late Ted Sizer and Maxine Greene, who advocated teaching students to be independent and creative thinkers, would applaud such a focus. But obtaining these results will not be easy, particularly when we consider the necessity of reconceptualizing the curriculum to emphasize interdisciplinary projects that develop students’ minds rather than testing them in ways that provide, at best, limited information of what they know, understand, and can do, which can lead to deeply misinformed views of students’ abilities and potential.
More than economics
The authors of the McKinsey report assert that education systems must evolve to meet the needs of a changing workplace. This means concentrated emphasis on “improv[ing] basic skills” and a new focus on “creativity, emotional intelligence, and leading and coaching others.”25 Indeed, the potential for change in all job categories requires us to become more agile, resilient, and flexible. So, too, will an increasingly automated world reward the rare and valuable ability to exercise good judgment, think strategically, make connections between seemingly disparate disciplines, and maintain a dispassionate presence of mind.26 Critically, encouraging students to think creatively, analytically, practically, and regeneratively may enable them to not only respond effectively to the challenges of automation, but also to address recurring challenges associated with global poverty, health epidemics, gender inequality, climate change, income polarization and human mobility.27 Indeed, addressing these challenges requires the capacity to collaborate, adapt, innovate, and create—which further suggests that students increasingly need access to both a broad knowledge base and adaptable strategies and dispositions that will enable them to effectively participate with diverse others from different regions and cultures.
When we encourage the development of these habits of mind in higher education, we enable more than the “ability to draw on or access one’s intellectual resources in situations where those resources may be relevant.”28 Indeed, we can inspire the ability to access not only cognitive resources, but also those that are emotional, psychological, physiological, social, and spiritual. Nurturing, training, and educating students to live lives that approach these standards can enable them to actively participate in creating future possible selves that can weather the economic and social repercussions of a rapidly changing world.29
1. Erik Brynjolfsson and Andrew McAfee, Race against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy (Lexington, MA: Digital Frontier Press, 2011).
2. See Maxine Greene, Releasing the Imagination: Essays on Education, the Arts, and Social Change (San Francisco, CA: Jossey-Bass, 1995); Maxine Greene, Variations on a Blue Guitar: The Lincoln Center Institute Lectures on Aesthetic Education (New York: Teachers College Press, 2001); Theodore R. Sizer, Horace’s Compromise: The Dilemma of the American High School (New York: Mariner Books, 2004); Theodore R. Sizer, The Red Pencil: Convictions from Experience in Education (New Haven, CT: Yale University Press, 2005).
3. For a discussion of social capital, see Pierre Bourdieu, “The Forms of Capital,” in Handbook of Theory and Research for the Sociology of Education, ed. John Richardson (New York: Greenwood Press, 1986), 241–58. For a discussion of emotional intelligence, see John D. Mayer and Peter Salovey, “Emotional Intelligence and the Construction and Regulation of Feelings,” Applied and Preventive Psychology 4, no. 3 (1995): 197–208. For a discussion of agentic perspective, see Albert Bandura, “Toward a Psychology of Human Agency,” Perspectives on Psychological Science 1 (2006): 164–80.
4. Barack Obama, “President Obama Farewell Address: Full Text,” CNN Politics, January 11, 2017, http://www.cnn.com/2017/01/10/politics/president-obama-farewell-speech/i....
5. Harold L. Sirkin, Michael Zinser, and Justin Ryan Rose, The Robotics Revolution: The Next Great Leap in Manufacturing (Boston: The Boston Consulting Group, 2015), 4.
6. Daron Acemoglu and Pascual Restrepo, Robots and Jobs: Evidence from US Labor Markets (Cambridge, MA: National Bureau of Economic Research, 2017), 37.
7. Claire Cain Miller, “Evidence That Robots Are Winning the Race for American Jobs,” New York Times, March 28, 2017, https://www.nytimes.com/2017/03/28/upshot/evidence-that-robots-are-winning-the-race-for-american-jobs.html.
8. James Manyika, Michael Chui, Mehdi Miremadi, Jacques Bughin, Katy George, Paul Willmott, and Martin Dewhurst, A Future That Works: Automation, Employment, and Productivity (New York: McKinsey and Company, 2017), 12, 14.
9. David H. Autor, “The Paradox of Abundance: Automation Anxiety Returns,” in Performance and Progress: Essays on Capitalism, Business, and Society, ed. Subramanian Rangan (New York: Oxford University Press, 2015), 237–60; David H. Autor, “Why Are There Still So Many Jobs? The History and Future of Workplace Automation,” Journal of Economic Perspectives 29, no. 3 (2015): 3–30.
10. Carl Benedikt Frey and Michael Osborne, The Future of Employment: How Susceptible Are Jobs to Computerisation? (Oxford, UK: Oxford Martin Programme on Technology and Employment, 2013).
11. Claudia Goldin and Lawrence F. Katz, The Race between Education and Technology (New York: Belknap Press, 2010).
12. Brynjolfsson and McAfee, Race against the Machine.
13. Brynjolfsson and McAfee, Race against the Machine; Karl Marx, Capital: Critique of Political Economy, Vol. 1, ed. Friedrich Engels (New York: Penguin Classics, 1992).
14. Cal Newport, Deep Work: Rules for Focused Success in a Distracted World (New York: Grand Central, 2016), 14.
15. See K. Anders Ericsson, Ralf Th. Krampe, and Clemens Tesch-Romer, “The Role of Deliberate Practice in the Acquisition of Expert Performance,” Psychological Review 100, no. 3 (1993): 363–406; K. Anders Ericsson, “Discovering Deliberate Practice Activities that Overcome Plateaus and Limits on Improvement of Performance,” International Symposium on Performance Science 2009, 11–21.
16. Ian A. McKenzie, David Ohayon, Huiliang Li, Joana Paes de Faria, Ben Emery, Koujiro Tohyama, and William D. Richardson, “Motor Skill Learning Requires Active Central Myelination,” Science 346, no. 6207 (2014): 318–22.
17. National Research Council, How People Learn: Bridging Research and Practice, ed. M. Suzanne Donovan, John D. Bransford, and James W. Pellegrino (Washington, DC: National Academy Press, 1999).
18. Susan Layden, Beatrice L. Bridglall, and Sheldon Solomon, “Creating Opportunities to Learn: The Opportunity Programs at Skidmore College, Saratoga Springs, New York,” in Teaching and Learning in Higher Education: Studies of Three Undergraduate Programs, ed. Beatrice L. Bridglall (New York: Lexington Books, 2013), 75–106; see also John D. Bransford, Ann L. Brown, and Rodney R. Cocking, eds., How People Learn: Brain, Mind, Experience, and School (Washington, DC: National Academy Press, 2000).
19. Robert J. Sternberg, Elena L. Grigorenko, and Beatrice L. Bridglall, “Intelligence as a Socialized Phenomenon,” in Affirmative Development: Cultivating Academic Ability, ed. Edmund W. Gordon and Beatrice L. Bridglall (Boulder, CO: Rowman and Littlefield Publishers), 49–73; also illuminating are Carol S. Dweck, “Is Math a Gift? Beliefs that Put Females at Risk,” in Why Aren’t More Women in Science? Top Researchers Debate the Evidence, ed. Stephen J. Ceci and Wendy M. Williams (Washington, DC: American Psychological Association, 2006): 47–55; and Carol S. Dweck, Mindsets and Math/Science Achievement, report prepared for the Carnegie Corporation of New York Institute for Advanced Study Commission on Mathematics and Science Education, 2008.
20. Lev S. Vygotsky, Mind in Society: The Development of Higher Psychological Processes (Cambridge, MA: Harvard University Press, 1978), 5.
21. Newport, Deep Work.
22. Cal Newport, How to Win at College: Surprising Secrets for Success from the Country’s Top Students (New York: Three Rivers Press, 2005); K. Anders Ericsson, “Adaptive Expertise and Cognitive Readiness: A Perspective from the Expert-Performance Approach,” in Teaching and Measuring Cognitive Readiness, ed. Harold F. O’Neil, Ray S. Perez, and Eva L. Baker (New York: Springer US, 2014), 179–97.
23. Sophie Leroy, “Why Is It So Hard to Do My Work? The Challenge of Attention Residue When Switching between Work Tasks,” Organizational Behavior and Human Decision Processes 109, no. 2 (2009): 168–81; Sophie Leroy and Aaron M. Schmidt, “The Effect of Regulatory Focus on Attention Residue and Performance During Interruptions,” Organizational Behavior and Human Decision Processes 137 (2016): 218–35.
24. Hazel Markus and Paula Nurius, “Possible Selves,” American Psychologist 41, no. 9 (1986): 954–69.
25. Manyika et al., A Future That Works, 115.
26. Layden, Bridglall, and Solomon, “Creating Opportunities to Learn.”
27. International Commission on Education for Sustainable Development Practice, Final Report (New York: The Earth Institute at Columbia University, 2008).
28. Richard S. Prawat, “Promoting Access to Knowledge, Strategy, and Disposition in Students: A Research Synthesis,” Review of Educational Research 59, no. 1 (1989): 3.
29. Markus and Nurius, “Possible Selves.”
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BEATRICE L. BRIDGLALL is dean of humanities at Bergen Community College and Fulbright Specialist in Higher Education.