importance of research in 21st century

21st-Century Learning: What It Is and Why It’s Important

21st-century learning  is a term used to describe a shift in education from the traditional methods of the past to a more modern approach. This new approach focuses on preparing students for the future by teaching them the skills they need to be successful in a global economy. 21st-century learning is not memorization or recitation but critical thinking, creativity, and collaboration. It is about preparing students for the real world, not just for a test.

Table of Contents

Introduction

It is becoming increasingly clear that 21st-century learning is essential for students to be successful in an ever-changing global economy. 21st-century learning is not simply an update to traditional education; it is a fundamental shift in how we think about and prepare students for their future.

21st-century learning is more than just the 3Rs (reading, writing, and arithmetic). It emphasizes the importance of critical thinking, creativity, collaboration, and communication – skills essential for students to thrive in the 21st century.

What is also clear is that 21st-century learning cannot occur in a traditional classroom setting. Students need to be actively engaged in their learning and have opportunities to apply what they are learning to real-world situations.

There are several ways that schools can incorporate 21st-century learning into their curriculum. One way to integrate 21st-century learning into the classroom is to focus on project-based learning. In project-based learning, students work on a project together. They use their creativity and critical thinking skills to solve problems. This type of learning is effective because it helps students learn how to work together and think critically.

Another way to incorporate 21st-century learning is to use technology in the classroom. Technology can facilitate collaboration and communication and provide students with opportunities to be creative and think critically.

The bottom line is that 21st-century learning is essential for students to be successful in the 21st century. It is about much more than just the 3Rs and cannot occur in a traditional classroom setting. Schools need to be creative in incorporating 21st-century learning into their curriculum.

21st-Century Skills Students Need for Learning

As the world changes, so do students’ skills to succeed. Here are 21st-century skills students need for learning:

• Communication:  Good communication skills are essential for students to work together and share their ideas.
• Critical Thinking:  The student needs to be able to think critically to analyze information and solve problems.
• Collaboration:  One must work effectively with others to achieve a common goal.
• Creativity:  Students need to think creatively to generate new ideas and solve problems innovatively.
• Digital Literacy:  Students must use technology effectively to access and create digital information.
• Information Literacy:  They must find, evaluate, and use information effectively.
• Media Literacy:  Students must critically analyze media messages to understand their impact on individuals and society. This critical analysis will help them understand how media messages can influence individuals and society.
• Problem-Solving:  Students must identify and solve problems to improve their learning.
• Self-Management:  Students need to be able to manage their learning to be successful independent learners.
• Social and Cultural Awareness:  Students need to be aware of the influence of social and cultural factors on their learning.
• Technological Literacy:  Students must use technology effectively to access and create digital information.
• Flexibility and Adaptability:  Students need to be able to adapt their learning to new situations and technologies.
• Initiative and Self-Direction:  Students need to take the initiative and be self-directed in their learning to be successful.
• Productivity and Accountability:  They must be productive and take responsibility for their learning.
• Leadership:  The students must take the lead in their education and motivate others to join them in learning.
• Social Responsibility:  Students must be aware of how their learning affects those around them and be respectful of others while learning.
• Sustainability:  It is essential for students to be aware of the impact their learning can have on the environment and to be considerate of environmental sustainability when they are learning.
• Ethical Responsibility:  Students need to be aware of the ethical implications of their learning and consider ethical responsibility in their learning.
• Global Perspective:  It is essential for students to be aware of the global context of their learning and to be considerate of international perspectives in their learning.
• Cultural Competence:  It is vital for students to be aware of the influence of culture on their learning and to be competent in cross-cultural communication.
• Diversity:  Students need to be aware of the diversity of perspectives and experiences in the world and be respectful of diversity in their learning.

These are just some skills students need to learn in the 21st century. As the world changes, so do students’ skills to succeed. Educators must stay up-to-date on the latest research and trends to prepare their students for the future.

The Importance of 21st-Century Learning

Here are just a few of the reasons why 21st-century learning is so important:

1.  It helps students develop the skills they need for the real world.

In the 21st century, employers are looking for workers who are not only knowledgeable but also adaptable, creative, and able to work collaboratively. 21st-century learning helps students develop these essential skills.

2.  It prepares students for an increasingly globalized world.

In today’s world, it’s more important than ever for students to be able to communicate and work with people from other cultures. 21st-century learning helps students develop the global perspective they need to be successful in an increasingly connected world.

3.  It helps students learn how to learn.

In a world where information is constantly changing, students need to be able to learn new things quickly and effectively. 21st-century learning helps students develop the metacognitive skills they need to be lifelong learners.

4.  It helps students develop a love of learning.

21st-century learning is hands-on, interactive, and engaging. This helps students develop a love of learning that will stay with them throughout their lives.

5.  It’s more relevant to students’ lives.

21st-century learning is relevant to students’ lives and the world they live in. It’s not just about memorizing facts but about developing the skills, students need to be successful in their personal and professional lives.

The importance of 21st-century learning cannot be overstated. In a constantly changing world, it’s more important than ever for students to develop the skills they need to be successful.

The Challenges of 21st-Century Learning

In the 21st century, learning is becoming increasingly complex and challenging. With the rapid pace of change in the world, it is difficult for students to keep up with the latest information and skills. In addition, they must also be able to apply what they have learned to real-world situations.

The following are some of the challenges of 21st-century learning:

1.  The pace of change is accelerating.

In the past, knowledge and skills were acquired slowly over time. However, in the 21st century, the pace of change is much faster, meaning students must learn more quickly to keep up with the latest information.

2.  The world is becoming more complex.

As the world becomes more complex and interconnected, students must be able to understand and navigate complex systems. They must also be able to think critically and solve problems.

3.  Students must be able to apply what they have learned.

In the past, students were often tested on their ability to remember and regurgitate information. However, in the 21st century, students need to be able to apply what they have learned to real-world situations. This requires them to be creative and to think critically.

4.  There is a greater emphasis on collaboration.

In the 21st century, there is a greater emphasis on collaboration. This means that students must be able to work effectively with others to achieve common goals. They must also be able to communicate effectively.

5.  Technology is changing the way we learn.

Technology is changing the way students learn. With the advent of the internet and mobile devices, students can now access information and resources that were previously unavailable. This has changed how students learn and made it possible for students to learn anywhere and at any time.

6.  Learning is no longer just about acquiring knowledge.

In the 21st century, learning is about more than just acquiring knowledge; it is also about developing skills, values, and attitudes. This means that students must be able to learn how to learn and adapt to change and different situations.

The 21st century presents many challenges for learners. However, it also provides many opportunities. With the right approach, students can overcome these challenges and be successful in the 21st century.

How Educators Can Support 21st-Century learning

There are several ways in which educators can support 21st-century learning. 

First,  they can create learning experiences relevant to the real world.  This means incorporating problems and scenarios that students will likely encounter in their future lives and careers.

Second,  educators can use technology to support 21st-century learning.  Technology can be used to create engaging and interactive learning experiences, and it can also be used to provide students with access to information and resources that they would not otherwise have.

Finally,  educators can model 21st-century learning for their students.  This means being flexible and adaptable in their teaching and using technology and real-world examples to illustrate their points. By modeling 21st-century learning, educators can show their students that learning can be relevant, engaging, and fun.

In the 21st century, educators must be prepared to meet the challenges of an ever-changing world. By creating relevant learning experiences, using technology to support learning, and modeling 21st-century learning for their students, educators can provide students with the skills they need to be successful in the 21st century.

Final Thoughts

As educators, we must prepare our students for the 21st century. We can do this by providing opportunities for them to develop essential 21st-century skills. Project-based learning is one of the best ways to do this.

Ultimately, we must commit to giving our students the 21st-century learning they deserve. This way, they will have the tools they need to thrive in a constantly changing world. They will also have the skills they need to succeed in whatever they choose to do.

HOW TO CITE THIS ARTICLE

Llego, M. A. (2022, September 14). 21st-Century Learning: What It Is and Why It’s Important. TeacherPH. Retrieved September 14, 2022 from, https://www.teacherph.com/21st-century-learning/

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Mark Anthony Llego

Mark Anthony Llego, a visionary from the Philippines, founded TeacherPH in October 2014 with a mission to transform the educational landscape. His platform has empowered thousands of Filipino teachers, providing them with crucial resources and a space for meaningful idea exchange, ultimately enhancing their instructional and supervisory capabilities. TeacherPH's influence extends far beyond its origins. Mark's insightful articles on education have garnered international attention, featuring on respected U.S. educational websites. Moreover, his work has become a valuable reference for researchers, contributing to the academic discourse on education.

1 thought on “21st-Century Learning: What It Is and Why It’s Important”

so informative thank you for giving me the opportunity to read your manuscripts. Worth sharing.

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Academic Research in the 21st Century: Maintaining Scientific Integrity in a Climate of Perverse Incentives and Hypercompetition

Over the last 50 years, we argue that incentives for academic scientists have become increasingly perverse in terms of competition for research funding, development of quantitative metrics to measure performance, and a changing business model for higher education itself. Furthermore, decreased discretionary funding at the federal and state level is creating a hypercompetitive environment between government agencies (e.g., EPA, NIH, CDC), for scientists in these agencies, and for academics seeking funding from all sources—the combination of perverse incentives and decreased funding increases pressures that can lead to unethical behavior. If a critical mass of scientists become untrustworthy, a tipping point is possible in which the scientific enterprise itself becomes inherently corrupt and public trust is lost, risking a new dark age with devastating consequences to humanity. Academia and federal agencies should better support science as a public good, and incentivize altruistic and ethical outcomes, while de-emphasizing output.

Introduction

T he incentives and reward structure of academia have undergone a dramatic change in the last half century. Competition has increased for tenure-track positions, and most U.S. PhD graduates are selecting careers in industry, government, or elsewhere partly because the current supply of PhDs far exceeds available academic positions (Cyranoski et al. , 2011 ; Stephan, 2012a ; Aitkenhead, 2013 ; Ladner et al. , 2013 ; Dzeng, 2014 ; Kolata, 2016 ). Universities are also increasingly “balance<ing> their budgets on the backs of adjuncts” given that part-time or adjunct professor jobs make up 76% of the academic labor force, while getting paid on average $2,700 per class, without benefits or job security (Curtis and Thornton, 2013 ; U.S. House Committee on Education and the Workforce, 2014 ). There are other concerns about the culture of modern academia, as reflected by studies showing that the attractiveness of academic research careers decreases over the course of students' PhD program at Tier-1 institutions relative to other careers (Sauermann and Roach, 2012 ; Schneider et al. , 2014 ), reflecting the overemphasis on quantitative metrics, competition for limited funding, and difficulties pursuing science as a public good.

In this article, we will (1) describe how perverse incentives and hypercompetition are altering academic behavior of researchers and universities, reducing scientific progress and increasing unethical actions, (2) propose a conceptual model that describes how emphasis on quantity versus quality can adversely affect true scientific progress, (3) consider ramifications of this environment on the next generation of Science, Technology, Engineering and Mathematics (STEM) researchers, public perception, and the future of science itself, and finally, (4) offer recommendations that could help our scientific institutions increase productivity and maintain public trust. We hope to begin a conversation among all stakeholders who acknowledge perverse incentives throughout academia, consider changes to increase scientific progress, and uphold “high ethical standards” in the profession (NAE, 2004 ).

Perverse Incentives in Research Academia: The New Normal?

When you rely on incentives, you undermine virtues. Then when you discover that you actually need people who want to do the right thing, those people don't exist… —Barry Schwartz, Swarthmore College (Zetter, 2009 )

Academics are human and readily respond to incentives. The need to achieve tenure has influenced faculty decisions, priorities, and activities since the concept first became popular (Wolverton, 1998 ). Recently, however, an emphasis on quantitative performance metrics (Van Noorden, 2010 ), increased competition for static or reduced federal research funding (e.g., NIH, NSF, and EPA), and a steady shift toward operating public universities on a private business model (Plerou, et al. , 1999 ; Brownlee, 2014 ; Kasperkevic, 2014 ) are creating an increasingly perverse academic culture. These changes may be creating problems in academia at both individual and institutional levels ( Table 1 ).

Growing Perverse Incentives in Academia

Modified from Regehr (pers. comm., 2015) with permission.

Quantitative performance metrics: effect on individual researchers and productivity

The goal of measuring scientific productivity has given rise to quantitative performance metrics, including publication count, citations, combined citation-publication counts (e.g., h-index), journal impact factors (JIF), total research dollars, and total patents. These quantitative metrics now dominate decision-making in faculty hiring, promotion and tenure, awards, and funding (Abbott et al. , 2010 ; Carpenter et al. , 2014 ). Because these measures are subject to manipulation, they are doomed to become misleading and even counterproductive, according to Goodhart's Law , which states that “ when a measure becomes a target, it ceases to be a good measure ” (Elton, 2004 ; Fischer et al. , 2012 ; Werner, 2015 ).

Ultimately, the well-intentioned use of quantitative metrics may create inequities and outcomes worse than the systems they replaced. Specifically, if rewards are disproportionally given to individuals manipulating their metrics, problems of the old subjective paradigms (e.g., old-boys' networks) may be tame by comparison. In a 2010 survey, 71% of respondents stated that they feared colleagues can “game” or “cheat” their way into better evaluations at their institutions (Abbott, 2010 ), demonstrating that scientists are acutely attuned to the possibility of abuses in the current system.

Quantitative metrics are scholar centric and reward output, which is not necessarily the same as achieving a goal of socially relevant and impactful research outcomes. Scientific output as measured by cited work has doubled every 9 years since about World War II (Bornmann and Mutz, 2015 ), producing “busier academics, shorter and less comprehensive papers” (Fischer et al. , 2012 ), and a change in climate from “publish or perish” to “funding or famine” (Quake, 2009 ; Tijdink et al. , 2014 ). Questions have been raised about how sustainable this exponential increase in the knowledge industry is (Price, 1963 ; Frodeman, 2011 ) and how much of the growth is illusory and results from manipulation as per Goodhart's Law .

Recent exposés have revealed schemes by journals to manipulate impact factors, use of p-hacking by researchers to mine for statistically significant and publishable results, rigging of the peer-review process itself, and overcitation (Falagas and Alexiou, 2008 ; Labbé, 2010 ; Zhivotovsky and Krutovsky, 2008 ; Bartneck and Kokkelmans, 2011 ; Delgado López-Cózar et al. , 2012 ; McDermott, 2013 ; Van Noorden, 2014 ; Barry, 2015 ). A fictional character was recently created to demonstrate a “spamming war in the heart of science,” by generation of 102 fake articles and a stellar h-index of 94 on Google Scholar (Labbé, 2010 ). Blogs describing how to more discretely raise h-index without committing outright fraud are also commonplace (e.g., Dem, 2011 ).

It is instructive to conceptualize the basic problem from a perspective of emphasizing quality-in-research versus quantity-in-research, as well as effects of perverse incentives ( Fig. 1 ). Assuming that the goal of the scientific enterprise is to maximize true scientific progress, a process that overemphasizes quality might require triple or quadruple blinded studies, mandatory replication of results by independent parties, and peer-review of all data and statistics before publication—such a system would minimize mistakes, but would produce very few results due to overcaution (left Fig. 1 ). At the other extreme, an overemphasis on quantity is also problematic because accepting less scientific rigor in statistics, replication, and quality controls or a less rigorous review process would produce a very high number of articles, but after considering costly setbacks associated with a high error rate, true progress would also be low. A hypothetical optimum productivity lies somewhere in between, and it is possible that our current practices (enforced by peer review) evolved to be near the optimum in an environment with fewer perverse incentives.

True scientific productivity vis-à-vis emphasis on research quality/quantity.

However, over the long term under a system of perverse incentives, the true productivity curve changes due to increased manipulation and/or unethical behavior ( Fig. 1 ). In a system overemphasizing quality, there is less incentive to cut corners because checks and balances allow problems to be discovered more easily, but in a system emphasizing quantity, productivity can be dramatically reduced by massive numbers of erroneous articles created by carelessness, subtle falsification (i.e., eliminating bad data), and substandard review if not outright fabrication (i.e., dry labbing).

While there is virtually no research exploring the impact of perverse incentives on scientific productivity, most in academia would acknowledge a collective shift in our behavior over the years ( Table 1 ), emphasizing quantity at the expense of quality. This issue may be especially troubling for attracting and retaining altruistically minded students, particularly women and underrepresented minorities (WURM), in STEM research careers. Because modern scientific careers are perceived as focusing on “the individual scientist and individual achievement” rather than altruistic goals (Thoman et al. , 2014 ), and WURM students tend to be attracted toward STEM fields for altruistic motives, including serving society and one's community (Diekman et al. , 2010 , Thoman et al. , 2014 ), many leave STEM to seek careers and work that is more in keeping with their values (e.g., Diekman et al. , 2010 ; Gibbs and Griffin, 2013 ; Campbell, et al. , 2014 ).

Thus, another danger of overemphasizing output versus outcomes and quantity versus quality is creating a system that is a “perversion of natural selection,” which selectively weeds out ethical and altruistic actors, while selecting for academics who are more comfortable and responsive to perverse incentives from the point of entry. Likewise, if normally ethical actors feel a need to engage in unethical behavior to maintain academic careers (Edwards, 2014 ), they may become complicit as per Granovetter's well-established Threshold Model of Collective Behavior (1978). At that point, unethical actions have become “embedded in the structures and processes” of a professional culture, and nearly everyone has been “induced to view corruption as permissible” (Ashforth and Anand, 2003 ).

It is also telling that a new genre of articles termed “quit lit” by the Chronicle of Higher Education has emerged (Chronicle Vitae, 2013–2014 ), in which successful, altruistic, and public-minded professors give perfectly rational reasons for leaving a profession they once loved—such individuals are easily replaced with new hires who are more comfortable with the current climate. Reasons for leaving range from a saturated job market, lack of autonomy, concerns associated with the very structure of academe (CHE, 2013 ), and “a perverse incentive structure that maintains the status quo, rewards mediocrity, and discourages potential high-impact interdisciplinary work” (Dunn, 2013 ).

While quantitative metrics provide an objective means of evaluating research productivity relative to subjective measures, now that they have become a target, they cease to be useful and may even be counterproductive. A continued overemphasis on quantitative metrics will pressure all but the most ethical scientists, to overemphasize quantity at the expense of quality, create pressures to “cut corners” throughout the system, and select for scientists attracted to perverse incentives.

Scientific societies, research institutions, academic journals and individuals have made similar arguments, and some have signed the San Francisco Declaration of Research Assessment (DORA). The DORA recognizes the need for improving “ways in which output of scientific research are evaluated” and calls for challenging research assessment practices, especially the JIF, which are currently in place. Signatories include the American Society for Cell Biology, American Association for the Advancement of Science, Howard Hughes Medical Institute, and Proceedings of The National Academy of Sciences, among 737 organizations and 12,229 individuals as of June 30, 2016. Indeed, publishers of Nature , Science , and other journals have called for downplaying the JIF metric, and the American Society of Microbiology is announcing plans to “purge the conversation of the impact factor” and remove them from all their journals (Callaway, 2016 ). The argument is not to get rid of metrics, but to reduce their importance in decision-making by institutions and funding agencies, and perhaps invest resources toward creating more meaningful metrics (ACSB, 2012 ). DORA would be a step in the right direction of halting the “avalanche” of performance metrics dominating research assessment, which are unreliable and have long been hypothesized to threaten the quality of research (Rice, 1994 ; Macilwain, 2013 ).

Performance metrics: effect on institutions

We had to get into the top 100. That was a life-or-death matter for Northeastern.— Richard Freeland, Former President of Northeastern University (Kutner, 2014 )

The perverse incentives for academic institutions are growing in scope and impact, as best exemplified by U.S. News & World Report annual rankings that purportedly measure “academic excellence” (Morse, 2015 ). The rankings have strongly influenced, positively or negatively, public perceptions regarding the quality of education and opportunities they offer (Casper, 1996 ; Gladwell, 2011 ; Tierney, 2013 ). Although U.S. News & World Report rankings have been dismissed by some, they still undeniably wield extraordinary influence on college administrators and university leadership—the perceptions created by the objective quantitative ranking determines “how students, parents, high schools, and colleges pursue and perceive education” in practice (Kutner, 2014 ; Segal, 2014 ).

The rankings rely on subjective proprietary formula and algorithms, the original validity of which has since been undermined by Goodhart's law —universities have attempted to game the system by redistributing resources or investing in areas that the ranking metrics emphasize. Northeastern University, for instance, unapologetically rose from #162 in 1996 to #42 in 2015 by explicitly changing their class sizes, acceptance rates, and even peer assessment. Others have cheated by reporting incorrect statistics (Bucknell University, Claremont-McKenna College, Clemson University, George Washington University, and Emory University are examples of those who were caught) to rise in the ranks (Slotnik and Perez-Pena, 2012 ; Anderson, 2013 ; Kutner, 2014 ). More than 90% of 576 college admission officers thought other institutions were submitting false data to U.S. News according to a 2013 Gallup and Inside Higher Ed poll (Jaschik, 2013 ), which creates further pressures to cheat throughout the system to maintain a ranking perceived to be fair as discussed in preceding sections.

Hypercompetitive funding environments

If the work you propose to do isn't virtually certain of success, then it won't get funded— Roger Kornberg, Nobel laureate (Lee, 2007 )

The only people who can survive in this environment are people who are absolutely passionate about what they're doing and have the self-confidence and competitiveness to just go back again and again and just persistently apply for funding— Robert Waterland, Baylor College of Medicine (Harris and Benincasa, 2014 )

The federal government's role in financing research and development (R&D), creating new knowledge, or fulfilling public missions like national security, agriculture, infrastructure, and environmental health has become paramount. The cost of high-risk, long-term research, which often has uncertain prospects and/or utility, has been largely borne by the U.S. government in the aftermath of World War II, forming part of an ecosystem with universities and industries contributing to the collective progress of mankind (Bornmann and Mutz, 2015 ; Hourihan, 2015 ).

However, in the current competitive global environment where China is projected to outspend the U.S. on R&D by 2020, some worry that the “edifice of American innovation rests on an increasingly rickety foundation” because of a decline in spending on federal R&D in the past decade (Casassus, 2014 ; OECD, 2014 ; MIT, 2015 ; Porter, 2015 ). U.S. “Research Intensity” (i.e., federal R&D as a share of the country's gross domestic product or GDP) has declined to 0.78% (2014), which is down from about 2% in the 1960 s ( Fig. 2 ). With discretionary spending of federal budgets projected to decrease, research intensity is likely to drop even further, despite increased industry funding (Hourihan, 2015 ).

Trends in research intensity (i.e., ratio of U.S. R&D to gross domestic product), roles of federal, business, and other nonfederal funding for R&D: 1953–2013. Data source: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). R&D, research and development.

A core mission of American colleges and universities has been “service to the public,” and this goal will be more difficult to reach as universities morph into profit centers churning out patents and new products (Faust, 2009 ; Mirowski, 2011 ; Brownlee, 2014 ; Hinkes-Jones, 2014 ; Seligsohn, 2015 ; American Academy of Arts and Sciences, 2016 ). Until the late 2000s, research institutions and universities went on a building spree fueled by borrowing, with an expectation that increased research funding would allow them to further boost research productivity—a cycle that went bust after the 2007–2008 financial crash (Stephan, 2012a ). Universities are also allowed to offset debt from ill-fated expansion efforts as indirect costs (Stephan, 2012b ), which increases overhead and decreases dollars available to spend on research even if funds raised by grants remain constant.

The static or declining federal investment in research has created the “worst research funding <scenario in 50 years>” and further ratcheted competition for funding (Lee, 2007 ; Quake, 2009 ; Harris and Benincasa, 2014 ; Schneider et al. , 2014 ; Stein, 2015 ), given that the number of researchers competing for grants is rising. The funding rate for NIH grants fell from 30.5% to 18% between 1997 and 2014, and the average age for a first time PI on an R01-equivalent grant has increased to 43 years (NIH, 2008 , 2015 ). NSF funding rates have remained stagnant between 23 and 25% in the past decade (NSF, 2016 ). While these funding rates are still well above the breakeven point of 6%, at which the net cost of proposal writing equals the net value obtained from a grant by the grant winner (Cushman et al. , 2015 ), there is little doubt the grant environment is hypercompetitive, susceptible to reviewer biases, and strongly dependent on prior success as measured by quantitative metrics (Lawrence, 2009 ; Fang and Casadevall, 2016 ). Researchers must tailor their thinking to align with solicited funding, and spend about half of their time addressing administrative and compliance, drawing focus away from scientific discovery and translation (NSB, 2014 ; Schneider et al. , 2014 ; Belluz et al. , 2016 ).

Systemic Risks to Scientific Integrity

Science is a human endeavor, and despite its obvious historical contributions to advancement of civilization, there is growing evidence that today's research publications too frequently suffer from lack of replicability, rely on biased data-sets, apply low or substandard statistical methods, fail to guard against researcher biases, and their findings are overhyped (Fanelli, 2009 ; Aschwanden, 2015 ; Belluz and Hoffman, 2015 ; Nuzzo, 2015 ; Gobry, 2016 ; Wilson, 2016 ). A troubling level of unethical activity, outright faking of peer review and retractions, has been revealed, which likely represents just a small portion of the total, given the high cost of exposing, disclosing, or acknowledging scientific misconduct (Marcus and Oransky, 2015 ; Retraction Watch, 2015a ; BBC, 2016 ; Borman, 2016 ). Warnings of systemic problems go back to at least 1991, when NSF Director Walter E. Massey noted that the size, complexity, and increased interdisciplinary nature of research in the face of growing competition was making science and engineering “more vulnerable to falsehoods” (The New York Times, 1991 ).

Misconduct is not limited to academic researchers. Federal agencies are also subject to perverse incentives and hypercompetition, giving rise to a new phenomenon of institutional scientific research misconduct (Lewis, 2014 ; Edwards, 2016 ). Recent exemplars uncovered by the first author in the Flint and Washington D.C. drinking water crises include “scientifically indefensible” reports by the U.S. Centers for Disease Control and Prevention (U.S. Centers for Disease Control and Prevention, 2004 ; U.S. House Committee on Science and Technology, 2010 ), reports based on nonexistent data published by the U.S. EPA and their consultants in industry journals (Reiber and Dufresne, 2006 ; Boyd et al. , 2012 ; Edwards, 2012 ; Retraction Watch, 2015b ; U.S. Congress House Committee on Oversight and Government Reform, 2016 ), and silencing of whistleblowers in EPA (Coleman-Adebayo, 2011 ; Lewis, 2014 ; U.S. Congress House Committee on Oversight and Government Reform, 2015 ). This problem is likely to increase as agencies increasingly compete with each other for reduced discretionary funding. It also raises legitimate and disturbing questions as to whether accepting research funding from federal agencies is inherently ethical or not—modern agencies clearly have conflicts similar to those that are accepted and well understood for industry research sponsors. Given the mistaken presumption of research neutrality by federal funding agencies (Oreskes and Conway, 2010 ), the dangers of institutional research misconduct to society may outweigh those of industry-sponsored research (Edwards, 2014 ).

A “trampling of the scientific ethos” witnessed in areas as diverse as climate science and galvanic corrosion undermines the “credibility of everyone in science” (Bedeian et al. , 2010 ; Oreskes and Conway, 2010 ; Edwards, 2012 ; Leiserowitz et al. , 2012 ; The Economist, 2013 ; BBC, 2016 ). The Economist recently highlighted the prevalence of shoddy and nonreproducible modern scientific research and its high financial cost to society—posing an open question as to whether modern science was trustworthy, while calling upon science to reform itself (The Economist, 2013 ). And, while there are hopes that some problems could be reduced by practices that include open data, open access, postpublication peer review, metastudies, and efforts to reproduce landmark studies, these can only partly compensate for the high error rates in modern science arising from individual and institutional perverse incentives ( Fig. 1 ).

The high costs of research misconduct

The National Science Foundation defines research misconduct as intentional “fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results” (Steneck, 2007 ; Fischer, 2011 ). Nationally, the percentage of guilty respondents in research misconduct cases investigated by the Department of Health and Human Services (includes NIH) and NSF ranges from 20% to 33% (U.S. Department of Health and Human Services, 2013 ; Kroll, 2015, pers. comm.). Direct costs of handling each research misconduct case are $525,000, and over $110 million are incurred annually for all such cases at the institutional level in the U.S (Michalek, et al. , 2010 ). A total of 291 articles retracted due to misconduct during 1992–2012 accounted for $58 M in direct funding from the NIH, which is less than 1% of the agency's budget during this period, but each retracted article accounted for about $400,000 in direct costs (Stern et al. , 2014 ).

Obviously, incidence of undetected misconduct is some multiple of the cases judged as such each year, and the true incidence is difficult to predict. A comprehensive meta-analysis of research misconduct surveys between 1987 and 2008 indicated that 1 in 50 scientists admitted to committing misconduct (fabrication, falsification, and/or modifying data) at least once and 14% knew of colleagues who had done so (Fanelli, 2009 ). These numbers are likely an underestimate considering the sensitivity of the questions asked, low response rates, and the Muhammad Ali effect (a self-serving bias where people perceive themselves as more honest than their peers) (Allison et al. , 1989 ). Indeed, delving deeper, 34% of researchers self-reported that they have engaged in “questionable research practices,” including “dropping data points on a gut feeling” and “changing the design, methodology, and results of a study in response to pressures from a funding source,” whereas 72% of those surveyed knew of colleagues who had done so (Fanelli, 2009 ). One study included in Fanelli's meta-analysis looked at rates of exposure to misconduct for 2,000 doctoral students and 2,000 faculty from the 99 largest graduate departments of chemistry, civil engineering, microbiology, and sociology, and found between 6 and 8% of both students and faculty had direct knowledge of faculty falsifying data (Swazey et al. , 1993 ).

In life science and biomedical research, the percentage of scientific articles retracted has increased 10-fold since 1975, and 67% were due to misconduct (Fang et al. , 2012 ). Various hypotheses are proposed for this increase, including “lure of the luxury journal,” “pathological publishing,” prevalent misconduct policies, academic culture, career stage, and perverse incentives (Martinson et al. , 2009 ; Harding et al. , 2012 ; Laduke, 2013 ; Schekman, 2013 ; Buela-Casal, 2014 ; Fanelli et al. , 2015 ; Marcus and Oransky, 2015 ; Sarewitz, 2016 ). Nature recently declared that “pretending research misconduct does not happen is no longer an option” (Nature, 2015 ).

Academia and science are expected to be self-policing and self-correcting. However, based on our experiences, we believe there are incentives throughout the system that induce all stakeholders to “pretend misconduct does not happen.” Science has never developed a clear system for reporting, investigating, or dealing with allegations of research misconduct, and those individuals who do attempt to police behavior are likely to be frustrated and suffer severe negative professional repercussions (Macilwain, 1997 ; Kevles, 2000 ; Denworth, 2008 ). Academics largely operate on an unenforceable and unwritten honor system, in relation to what is considered fair in reporting research, grant writing practices, and “selling” research ideas, and there is serious doubt as to whether science as a whole can actually be considered self-correcting (Stroebe et al. , 2012 ). While there are exceptional cases where individuals have provided a reality check on overhyped research press releases in areas deemed potentially transformative (e.g., Eisen, 2010–2015 ; New Scientist, 2016 ), limitations of hot research sectors are more often downplayed or ignored. Because every modern scientific mania also creates a quantitative metric windfall for participants and there are few consequences for those responsible after a science bubble finally pops, the only true check on pathological science and a misallocation of resources is the unwritten honor system (Langmuir et al. , 1953 ).

If nothing is done, we will create a corrupt academic culture

The modern academic research enterprise, dubbed a “Ponzi Scheme” by The Economist , created the existing perverse incentive system, which would have been almost inconceivable to academics of 30–50 years ago (The Economist, 2010 ). We believe that this creation is a threat to the future of science, and unless immediate action is taken, we run the risk of “normalization of corruption” (Ashforth and Anand, 2003 ), creating a corrupt professional culture akin to that recently revealed in professional cycling or in the Atlanta school cheating scandal.

To review, for the 7 years Lance Armstrong won the Tour de France (1999–2005), 20 out of 21 podium finishers (including Armstrong) were directly tied to doping through admissions, sanctions, public investigations, or failing blood tests. Entire teams cheated together because of a “win-at-all cost culture” that was created and sustained over time because there was no alternative in sight (U.S. ADA, 2012 ; Rose and Fisher, 2013 ; Saraceno, 2013 ). Numerous warning signs were ignored, and a retrospective analysis indicates that more than half of all Tour de France winners since 1980 had either been tested positive for or confessed to doping (Mulvey, 2012 ). The resultant “culture of doping” put clean athletes under suspicion (CIRC, 2015 ; Dimeo, 2015 ) and ultimately brought worldwide disrepute to the sport.

Likewise, the Atlanta Public Schools (APS) scandal provides another example of a perverse incentive system run to its logical conclusion, but in an educational setting. Twelve former APS employees were sent to prison and dozens faced ethics sanctions for falsifying students' results on state-standardized tests. The well-intentioned quantitative test results became high stakes to the APS employees, because the law “trigger[s] serious consequences for students (like grade promotion and graduation); schools (extra resources, reorganization, or closure); districts (potential loss of federal funds), and school employees (bonuses, demotion, poor evaluations, or firing)” (Kamenetz, 2015 ). The APS employees betrayed their stated mission of creating a “caring culture of trust and collaboration [where] every student will graduate ready for college and career,” and participated in creating the illusion of a “high-performing school district” (APS, 2016 ). Clearly, perverse incentives can encourage unethical behavior to manipulate quantitative metrics, even in an institution where the sole goal was to educate children.

An uncontrolled perverse incentive system can create a climate in which participants feel they must cheat to compete, whether it is academia (individual or institutional level) or professional sports. While procycling was ultimately discredited and its rewards were not properly distributed to ethical participants, in science, the loss of altruistic actors and trust, and risk of direct harm to the public and the planet raise the dangers immeasurably.

What Kind of Profession Are We Creating for the Next Generation of Academics?

So I have just one wish for you—the good luck to be somewhere where you are free to maintain the kind of integrity I have described, and where you do not feel forced by a need to maintain your position in the organization, or financial support, or so on, to lose your integrity. May you have that freedom— Richard Feynman, Nobel laureate (Feynman, 1974 )

The culture of academia has undergone dramatic change in the last few decades—quite a bit of it has been for the better. Problems with diversity, work-life balance, funding, efficient teaching, public outreach, and engagement have been recognized and partly addressed.

As stewards of the profession, we should continually consider whether our collective actions will leave our field in a state that is better or worse than when we entered it. While factors such as state and federal funding levels are largely beyond our control, we are not powerless and passive actors. Problems with perverse incentives and hypercompetition could be addressed by the following:

• (1) The scope of the problem must be better understood, by systematically mining the experiences and perceptions held by academics in STEM fields, through a comprehensive survey of high-achieving graduate students and researchers.
• (2) The National Science Foundation should commission a panel of economists and social scientists with expertise in perverse incentives, to collect and review input from all levels of academia, including retired National Academy members and distinguished STEM scholars. The panel could also develop a list of “best practices” to guide evaluation of candidates for hiring and promotion, from a long-term perspective of promoting science in the public interest and for the public good, and maintain academia as a desirable career path for altruistic ethical actors.
• (3) Rather than pretending that the problem of research misconduct does not exist, science and engineering students should receive instruction on these subjects at both the undergraduate and graduate levels. Instruction should include a review of real world pressures, incentives, and stresses that can increase the likelihood of research misconduct.
• (4) Beyond conventional goals of achieving quantitative metrics, a PhD program should also be viewed as an exercise in building character, with some emphasis on the ideal of practicing science as service to humanity (Huber, 2014 ).
• (5) Universities need to reduce perverse incentives and uphold research misconduct policies that discourage unethical behavior.

Acknowledgments

The authors wish to thank PhD Candidate William Rhoads from Virginia Tech and three anonymous reviewers from Environmental Engineering Science for their assistance with the article and valuable suggestions.

Author Disclosure Statement

No competing financial interests exist.

• Abbott A., Cyranoski D., Jones N., Maher B., Schiermeier Q., and Van Noorden R. (2010). Metrics: Do metrics matter? Nature . 465 , 860. [ PubMed ] [ Google Scholar ]
• Aitkenhead D. (2013, Dec. 6). Peter Higgs: I wouldn't be productive enough for today's academic system . The Guardian . Available at: www.theguardian.com/science/2013/dec/06/peter-higgs-boson-academic-system (accessed September16, 2016)
• Allison S.T., Messick D.M., and Goethals G.R. (1989). On being better but not smarter than others: The Muhammad Ali effect . Soc. Cogn. 7 , 275 [ Google Scholar ]
• American Academy of Arts and Sciences. (2016). Public Research Universities: Serving the Public Good . Cambridge, MA: American Academy of Arts and Sciences [ Google Scholar ]
• American Society of Cell Biology (ACSB). (2012). San Francisco Declaration on Research Assessment . Available at: www.ascb.org/dora (accessed September16, 2016)
• Anderson N. (2013, Feb. 6). Five colleges misreported data to U.S. News, raising concerns about rankings, reputation . The Washington Post . Available at: www.washingtonpost.com/local/education/five-colleges-misreported-data-to-us-news-raising-concerns-about-rankings-reputation/2013/02/06/cb437876-6b17-11e2-af53-7b2b2a7510a8_story.html (accessed September16, 2016)
• Aschwanden C. (2015, Aug. 19). Science isn't broken . FiveThirtyEight . Available at: http://fivethirtyeight.com/features/science-isnt-broken (accessed September16, 2016)
• Ashforth B.E., and Anand V. (2003). The normalization of corruption in organizations . Res. Organ. Behav. 25 , 1 [ Google Scholar ]
• Atlanta Public Schools (APS). (2016). About APS | Mission and Vision . Available at: www.atlanta.k12.ga.us/Domain/35 (accessed September16, 2016)
• Barry F. (2015, Apr. 1). Peer-review cheating ring discredits another 43 papers . In-Pharma Technologist . Available at: www.in-pharmatechnologist.com/Regulatory-Safety/Peer-review-cheating-ring-discredits-another-43-papers (accessed September16, 2016)
• Bartneck C., and Kokkelmans S. (2011). Detecting h-index manipulation through self-citation analysis . Scientometrics . 87 , 85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bedeian A.G., Taylor S.G., and Miller A.N. (2010). Management science on the credibility bubble: Cardinal sins and various misdemeanors . Acad. Manage. Learn. Educ. 9 , 715 [ Google Scholar ]
• Belluz J., and Hoffman S. (2015, May 13). Science is often flawed. Its time we embraced that . Vox . Available at: www.vox.com/2015/5/13/8591837/how-science-is-broken (accessed September16, 2016)
• Belluz J., Plumer B., and Resnick B. (2016, Jul. 14). The 7 biggest problems facing science, according to 270 scientists . Vox . Available at: www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process (accessed September16, 2016)
• Borman S. (2016). Nanoparticle synthesis paper retracted after 12 years . Chemical and Engineering News . Available at: http://cen.acs.org/articles/94/i8/Nanoparticle-Synthesis-Paper-Retracted-After-12-Years.html (accessed September16, 2016)
• Bornmann L., and Mutz R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references . J. Assoc. Inform. Sci. Technol. 66 , 2215 [ Google Scholar ]
• Boyd G.R., Reiber S.H., McFadden M., and Korshin G. (2012). Effect of changing water quality on galvanic coupling . J. AWWA. 104 , E136 [ Google Scholar ]
• British Broadcasting Corporation (BBC). (2016). Saving science from the scientists: Episode 2 . BBC . Available at: www.bbc.co.uk/programmes/b07378cr (accessed September16, 2016)
• Brownlee J.K. (2014). Irreconcilable Differences: The Corporatization of Canadian Universities (Doctoral dissertation). Carleton University; Available at: https://curve.carleton.ca/b945d1f1-64d4-40eb-92d2-1a29effe0f76 (accessed September16, 2016) [ Google Scholar ]
• Buela-Casal G. (2014). Pathological publishing: A new psychological disorder with legal consequences? Eur . J. Psychol. Appl. Legal Context. 6 , 91 [ Google Scholar ]
• Callaway E. (2016). Beat it, impact factor! Publishing elite turns against controversial metric . Nature . Available at: www.nature.com/news/beat-it-impact-factor-publishing-elite-turns-against-controversial-metric-1.20224 (accessed September16, 2016) [ PubMed ]
• Campbell A.G., Skvirsky R., Wortis H., Thomas S., Kawachi I., and Hohmann C. (2014). Nest 2014: Views from the trainees-talking about what matters in efforts to diversify the stem workforce . CBE Life Sci. Educ. 13 , 587. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Carpenter C.R., Cone D.C., and Sarli C.C. (2014). Using publication metrics to highlight academic productivity and research impact . Acad. Emerg. Med. 21 , 1160. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Casassus B. (2014). China predicted to outspend the US on science by 2020 . Nature . Available at: www.nature.com/news/china-predicted-to-outspend-the-us-on-science-by-2020-1.16329 (accessed September16, 2016)
• Casper G. (1996). Criticism of College Rankings . Letter from Stanford President Gerhard Casper to James Fallows, Editor of U.S. News & World Report . Available at: http://web.stanford.edu/dept/pres-provost/president/speeches/961206gcfallow.html (accessed September16, 2016)
• Chronicle of Higher Education (CHE). (2013). What we talk about when we talk about quitting . Chronicle of Higher Education . Available at: https://chroniclevitae.com/news/215-what-we-talk-about-when-we-talk-about-quitting?cid=vem (accessed September16, 2016)
• Chronicle Vitae (2013–2014). Quit lit . Chronicle Vitae . Available at: https://chroniclevitae.com/news/tags/Quit%20Lit (accessed September16, 2016)
• Coleman-Adebayo M. (2011). No Fear: A Whistleblower's Triumph Over Corruption and Retaliation at the EPA . Chicago, IL: Chicago Review Press [ Google Scholar ]
• Curtis J.W., and Thornton S. (2013). Here's the news: The annual report on the economic status of the profession 2012–13 . Academe . 99 , 4 [ Google Scholar ]
• Cushman P., Hoeksema J.T., Kouveliotou C., Lowenthal J., Peterson B., Stassun K.G., and von Hippel T. (2015). Impact of declining proposal success rates on scientific productivity . AAAC Proposal Pressures Study Group Discussion Draft . Available at: www.nsf.gov/attachments/134636/public/proposal_success_rates_aaac_final.pdf (accessed September16, 2016)
• Cycling Independent Reform Commission (CIRC). (2015). Report to the President of the Union Cyclist Internationale . Available at: www.uci.ch/mm/Document/News/CleanSport/16/87/99/CIRCReport2015_Neutral.pdf (accessed September16, 2016)
• Cyranoski D., Gilbert N., Ledford H., Nayar A., and Yahia M. (2011). Education: The PhD factory . Nature . 472 , 276. [ PubMed ] [ Google Scholar ]
• Delgado López-Cózar N., Robinson-García N., and Torres-Salinas D. (2012). Manipulating Google scholar citations and Google scholar metrics: Simple, easy and tempting . EC3 Working Papers . 6 : 29 May, 2012 [ Google Scholar ]
• Dem G. (2011). How to Increase Your Papers Citation and H Index . Available at: www.academia.edu/934257/How_to_increase_your_papers_citations_and_ h_index_in_5_simple_steps (accessed September16, 2016)
• Denworth L. (2008). Toxic Truth: A Scientist, a Doctor, and the Battle Over Lead . Boston, MA: Beacon Press [ Google Scholar ]
• Diekman A.B., Brown E.R., Johnston A.M., and Clark E.K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers . Psychol. Sci. 21 , 1051. [ PubMed ] [ Google Scholar ]
• Dimeo P. (2015, Jul. 10). How cycling's dark history continues to haunt the Tour de France . The Conversation . Available at: http://theconversation.com/how-cyclings-dark-history-continues-to-haunt-the-tour-de-france-44312 (accessed September16, 2016)
• Dunn S. (2013, Dec. 12). Why so many academics quit and tell . Chronicle of Higher Education . Available at: https://chroniclevitae.com/news/216-why-so-many-academics-quit-and-tell (accessed September16, 2016)
• Dzeng E. (2014). How academia and publishing are destroying scientific innovation: A conversation with Sydney Brenner . Elizabeth Dzeng's Blog; Available at: https://elizabethdzeng.com/2014/02/26/how-academia-and-publishing-are-destroying-scientific-innovation-a-conversation-with-sydney-brenner/ (accessed September16, 2016) [ Google Scholar ]
• Edwards M.A. (2012). Discussion: Effect of changing water quality on galvanic coupling . J. Am. Water Works Assoc. 104 , 65 [ Google Scholar ]
• Edwards M.A. (2014). Foreword . In Lewis D.L., Ed., Science for Sale . New York: Skyhorse Publishing, pp. ix–xiii [ Google Scholar ]
• Edwards M.A. (2016). Institutional Scientific Misconduct at U.S. Public Health Agencies: How Malevolent Government Betrayed Flint, MI . Testimony to the U.S. Cong. Committee on Oversight and Government Reform on Examining Federal Administration of the Safe Drinking Water Act in Flint , Michigan Hearing, February 3, 2016. 112th Congress. 2nd session Available at: https://oversight.house.gov/wp-content/uploads/2016/02/Edwards-VA-Tech-Statement-2-3-Flint-Water.pdf (accessed September16, 2016) [ Google Scholar ]
• Eisen J.A. (2010–2015). Microbiomania . Personal Blog . Available at: http://phylogenomics.blogspot.com/p/blog-page.html (accessed September16, 2016)
• Elton L. (2004). Goodhart's law and performance indicators in higher education . Eval. Res. Educ. 18 , 120 [ Google Scholar ]
• Falagas M.E., and Alexiou V.G. (2008). The top-ten in journal impact factor manipulation . Arch Immunol Ther Exp (Warsz) . 56 , 223. [ PubMed ] [ Google Scholar ]
• Fanelli D. (2009). How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data . PLoS One . 4 , e5738. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fanelli D., Costas R., and Larivière V. (2015). Misconduct policies, academic culture and career stage, not gender or pressures to publish, affect scientific integrity . PLoS One . 10 , e0127556. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fang F.C., and Casadevall A. (2016). Research funding: The case for a modified lottery . mBio . 7 , e00422. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fang F.C., Grant Steen R., and Casadevall A. (2012). Misconduct accounts for the majority of retracted scientific publications . Proc. Natl. Aacd. Sci. U. S. A. 109 , 17028 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Faust D.G. (2009, Sep. 1). The University's Crisis of Purpose . The New York Times . Available at: www.nytimes.com/2009/09/06/books/review/Faust-t.html?_r=0 (accessed September16, 2016)
• Feynman R.P. (1974). Cargo Cult Science: Caltech's 1974 Commencement Address . Available at: http://calteches.library.caltech.edu/51/2/CargoCult.htm (accessed September16, 2016)
• Fischer P. (2011). New Research Misconduct Policies . National Science Foundation; Available at: www.nsf.gov/oig/_pdf/presentations/session.pdf (accessed September16, 2016) [ Google Scholar ]
• Fischer J., Ritchie E.G., and Hanspach J. (2012). An academia beyond quantity: A reply to Loyola et al . and Halme et al. Trends Ecol. Evol . 27 , 587 [ PubMed ] [ Google Scholar ]
• Frodeman R. (2011). Interdisciplinary research and academic sustainability: Managing knowledge in an age of accountability . Environ. Conserv. 38 , 105 [ Google Scholar ]
• Gibbs K.D., and Griffin K.A. (2013). What do I want to be with my PhD? The roles of personal values and structural dynamics in shaping the career interests of recent biomedical science phd graduates . CBE Life Sci. Educ. 12 , 711. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gladwell M. (2011, Feb. 24). The order of things . The New Yorker . Available at: www.newyorker.com/magazine/2011/02/14/the-order-of-things (accessed September16, 2016)
• Gobry P.-E. (2016, Apr. 18). Big science is broken . The Week . Available at: http://theweek.com/articles/618141/big-science-broken (accessed September16, 2016)
• Harding T.S., Carpenter D.D., and Finelli C.J. (2012). An exploratory investigation of the ethical behavior of engineering undergraduates . J. Eng. Educ. 101 , 346 [ Google Scholar ]
• Harris R., and Benincasa R. (2014, Sep. 9). U.S. science suffering from booms and busts in funding . National Public Radio . Available at: www.npr.org/sections/health-shots/2014/09/09/340716091/u-s-science-suffering-from-booms-and-busts-in-funding (accessed September16, 2016)
• Hinkes-Jones L. (2014). Bad science . Jacobin . Available at: www.jacobinmag.com/2014/06/bad-science (accessed September16, 2016)
• Hourihan M. (2015). Federal R&D Budget Trends: A Short Summary . Available at: www.aaas.org/program/rd-budget-and-policy-program (accessed September16, 2016)
• Huber B.R. (2014, Sep. 22). Scientists seen as competent but not trusted by Americans . Woodrow Wilson Research Briefs . Available at: wws.princeton.edu/news-and-events/news/item/scientists-seen-competent-not-trusted-americans (accessed September16, 2016)
• Jaschik S. (2013, Sep. 18). Feeling the Heat: The 2013 Survey of College and University Admissions Directors . Inside Higher Ed, www.insidehighered.com/news/survey/feeling-heat-2013-survey-college-and-university-admissions-directors (accessed September16, 2016) [ Google Scholar ]
• Kamenetz A. (2015, Apr. 1). The Atlanta cheating verdict: Some context . National Public Radio . Available at: www.npr.org/blogs/ed/2015/04/01/396869874/the-atlanta-cheating-verdict-some-context (accessed September16, 2016)
• Kasperkevic J. (2014, Oct. 7). The harsh truth: US colleges are businesses, and student loans pay the bills . The Guardian . Available at: www.theguardian.com/money/us-money-blog/2014/oct/07/colleges-ceos-cooper-union-ivory-tower-tuition-student-loan-debt (accessed September16, 2016)
• Kevles D.J. (2000). The Baltimore Case: A Trial of Politics, Science, and Character . New York: W. W. Norton & Company [ Google Scholar ]
• Kolata G. (2016, Jul. 14). So many research scientists, so few openings as professors . The New York Times . Available at: www.nytimes.com/2016/07/14/upshot/so-many-research-scientists-so-few-openings-as-professors.html (accessed September16, 2016)
• Kutner M. (2014, Sep.). How Northeastern university gamed the college rankings . Boston Magazine . Available at: www.bostonmagazine.com/news/article/2014/08/26/how-northeastern-gamed-the-college-rankings (accessed September16, 2016)
• Labbé C. (2010). Ike Antkare One of the Great Stars in the Scientific Firmament . Laboratoire d'Informatique de Grenoble Research Report No. RR-LIG-008. Available at: http://rr.liglab.fr/research_report/RR-LIG-008.pdf (accessed September16, 2016)
• Ladner D.A., Bolyard S.C., Apul D., and Whelton A.J. (2013). Navigating the academic job search for environmental engineers: Guidance for job seekers and mentors . J. Prof. Issues Eng. Educ. Pract. 139 , 211 [ Google Scholar ]
• Laduke R.D. (2013). Academic dishonesty today, unethical practices tomorrow? J . Prof. Nurs. 29 , 402 [ PubMed ] [ Google Scholar ]
• Langmuir I. (1953). Colloquium on Pathological Science. Knolls Research Laboratory , December 18, 1953. Available at: www.cs.princeton.edu/∼ken/Langmuir/langmuir.htm (accessed September16, 2016)
• Lawrence P.A. (2009). Real lives and white lies in the funding of scientific research . PLoS Biol . 7 , e1000197. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lee C. (2007, May 28). Slump in NIH funding is taking toll on research . The Washington Post . Available at: www.washingtonpost.com/wp-dyn/content/article/2007/05/27/AR2007052700794.html (accessed September16, 2016)
• Leiserowitz A.A., Maibach E.W., Roser-Renouf C., Smith N., and Dawson E. (2012). Climategate, public opinion and the loss of trust . Am. Behav. Scientist. 57 , 818 [ Google Scholar ]
• Lewis D.L. (2014). Science for Sale . New York: Skyhorse Publishing [ Google Scholar ]
• Macilwain C. (1997). Whistleblowers face blast of hostility . Nature . 385 , 669. [ PubMed ] [ Google Scholar ]
• Macilwain C. (2013). Halt the avalanche of performance metrics . Nature . 500 , 255. [ PubMed ] [ Google Scholar ]
• Marcus A., and Oransky I. (2015, May 22). What's behind big science frauds? The New York Times . Available at: www.nytimes.com/2015/05/23/opinion/whats-behind-big-science-frauds.html (accessed September16, 2016)
• Martinson B.C., Crain A.L., Anderson M.S., and De Vries R. (2009). Institutions' expectations for researchers' self-funding, federal grant holding, and private industry involvement: Manifold drivers of self-interest and researcher behavior . Acad. Med. 84 , 1491. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Massachusetts Institute of Technology (MIT). (2015). The Future Postponed . A Report by the MIT Committee to evaluate the innovation deficit . Available at: https://dc.mit.edu/sites/default/files/Future%20Postponed.pdf (accessed September16, 2016)
• McDermott J. (2013, Mar. 7). Gaming the system: How to get an astronomical h-index with little scientific impact . Personal Blog . Available at: http://jasonya.com/wp/gaming-the-system-how-to-get-an-astronomical-h-index-with-little-scientific-impact (accessed September16, 2016)
• Michalek A.M., Hutson A.D., Wicher C.P., and Trump D.L. (2010). The costs and underappreciated consequences of research misconduct: A case study . PLoS Med . 7 , e1000318. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mirowski P. (2011). Science-Mart . Boston, MA: Harvard University Press [ Google Scholar ]
• Morse R. (2015, Mar. 27). Debunking myths about The U.S. news best colleges rankings . U.S. News & World Report . Available at: www.usnews.com/education/blogs/college-rankings-blog/2015/03/27/debunking-myths-about-the-us-news-best-colleges-rankings (accessed September16, 2016)
• Mulvey S. (2012, Oct. 12). Lance Armstrong: Tyler Hamilton on ‘how US Postal cheated’ . BBC News . Available at: www.bbc.com/news/magazine-19912623 (accessed September16, 2016)
• National Academy of Engineering (NAE). (2004). The Engineer of 2020: Visions of Engineering in the New Century . Washington, DC: The National Academies Press [ Google Scholar ]
• National Institutes of Health (NIH). (2008). Average Age of Principal Investigators . Available at: https://report.nih.gov/NIH_Investment/PDF_sectionwise/NIH_Extramural_DataBook_PDF/NEDB_SPECIAL_TOPIC-AVERAGE_AGE.pdf (accessed September16, 2016)
• National Institutes of Health (NIH). (2015). NIH Research Portfolio Online Reporting Tools–Research Project Success Rates of NIH Institute . Available at: https://report.nih.gov/success_rates/Success_ByIC.cfm (accessed September16, 2016)
• National Science Board (NSB). (2014). Reducing Investigators' Administrative Workload for Federally Funded Research . Report NSB-14-18. Arlington, VA: National Science Foundation [ Google Scholar ]
• National Science Foundation (NSF). (2016). Budget Internet Information System: Providing Statistical and Funding Information . Available at: http://dellweb.bfa.nsf.gov/awdfr3/default.asp (accessed September16, 2016)
• Nature. (2015, May 19). Editorial: Publish or perish . Nature . 521 , 159. [ Google Scholar ]
• New Scientist. (2016, May 17). Microbiomania: The Truth Behind the Hype About Our Bodily Bugs . Available at: www.newscientist.com/article/2088613-microbiomania-the-truth-behind-the-hype-about-our-bodily-bugs (accessed September16, 2016)
• Nuzzo R. (2015, Oct. 7). How scientists fool themselves–and how they can stop . Nature . 526 , 182. [ PubMed ] [ Google Scholar ]
• OECD. (2014). OECD Science, Technology and Industry Outlook 2014 . Paris: OECD Publishing [ Google Scholar ]
• Oreskes N., and Conway E.M. (2010). Merchants of Doubt . New York: Bloomsbury Press [ PubMed ] [ Google Scholar ]
• Plerou V., Nunes Amaral L.A., Gopikrishnan P., Meyer M., and Stanley H.G. (1999). Similarities between the growth dynamics of university research and of competitive economic activities . Nature . 400 , 433 [ Google Scholar ]
• Porter E. (2015, May 19). American Innovation Lies on Weak Foundation . The New York Times . Available at: www.nytimes.com/2015/05/20/business/economy/american-innovation-rests-on-weak-foundation.html (accessed September16, 2016)
• Price D.J.D.-S. (1963). Little Science, Big Science… and Beyond . New York: Columbia University Press [ Google Scholar ]
• Quake S. (2009). Letting scientists off the leash . The New York Times Blog , February 10
• Reiber S., and Dufresne L. (2006). Effects of External Currents and Dissimilar Metal Contact on Corrosion of Lead from Lead Service Lines . Philadelphia: Final Report to USEPA region III [ Google Scholar ]
• Retraction Watch. (2015a, Nov. 5). Got the Blues? You Can Still See Blue: Popular Paper on Sadness, Color Perception Retracted . Available at: http://retractionwatch.com/2015/11/05/got-the-blues-you-can-still-see-blue-after-all-paper-on-sadness-and-color-perception-retracted (accessed September16, 2016)
• Retraction Watch. (2015b). Expression of Concern Opens Floodgates of Controversy Over Lead in Water Supply . Available at: http://retractionwatch.com/2015/04/15/expression-of-concern-opens-floodgates-of-controversy-over-lead-in-water-supply (accessed September16, 2016)
• Rice J.R. (1994). Metrics to Evaluate Academic Departments . Purdue University Computer Science Technical Reports . Paper 1148
• Rose C., and Fisher N. (2013). Following Lance Armstrong: Excellence corrupted . Harvard Business Review , Case Study # 314015-PDF-ENG, Available at: https://hbr.org/product/following-lance-armstrong-excellence-corrupted/an/314015-PDF-ENG (accessed September16, 2016)
• Saraceno J. (2013, Jan. 18). Lance Armstrong: ‘I was trying to win at all costs.’ USA Today . Available at: www.usatoday.com/story/sports/cycling/2013/01/18/lance-armstrong-oprah-winfrey-television-interview-tour-de-france-drugs/1843329 (accessed September16, 2016)
• Sarewitz D. (2016). The pressure to publish pushes down quality . Nature . 533 , 147. [ PubMed ] [ Google Scholar ]
• Sauermann H., and Roach M. (2012). Science PhD career preferences: Levels, changes, and advisor encouragement . PLoS One . 7 , e36307. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schekman R. (2013, Dec. 9). How journals like Nature, Cell and Science are damaging science . The Guardian . Available at: www.theguardian.com/commentisfree/2013/dec/09/how-journals-nature-science-cell-damage-science (accessed September16, 2016)
• Schneider R.L., Ness K.L., Rockwell S., Shaver S., and Brutkiewicz R. (2014). 2012 Faculty Workload Survey Research Report . National Academy of Sciences, Engineering, and Medicine; Available at: http://sites.nationalacademies.org/cs/groups/pgasite/documents/webpage/pga_087667.pdf (accessed September16, 2016) [ Google Scholar ]
• Segal M. (2014, Sep. 15). The big college ranking sham: Why you must ignore U.S. News and World's Report list . Salon . Available at: www.salon.com/2014/09/15/the_big_college_ranking_sham_why_you_must_ignore_u_s_news_and_worlds_report_list (accessed September16, 2016)
• Seligsohn A. (2015, Apr. 29). An observation about the mission of higher education . Campus Compact . Available at: http://compact.org/resource-posts/an-observation-about-the-mission-of-higher-education (accessed September16, 2016)
• Slotnik D.E., and Perez-Pena R. (2012, Jan. 30). “ College Says It Exaggerated SAT Figures for Ratings .” The New York Times . www.nytimes.com/2012/01/31/education/claremont-mckenna-college-says-it-exaggerated-sat-figures.html?smid=pl-share&_r=0 (accessed September16, 2016)
• Smaldino P.E., and McElreath R. (2016). The natural selection of bad science . arXiv . Available at: https://arxiv.org/abs/1605.09511 (accessed September16, 2016) [ PMC free article ] [ PubMed ]
• Stein S. (2015, Mar. 3). Congrats young scientists, you face the worst research funding in 50 years . The Huffington Post . Available at: www.huffingtonpost.com/2015/03/03/francis-colliins-nih-funding_n_6795900.html (accessed September16, 2016)
• Steneck N.H. (2007). ORI: Introduction to the Responsible Conduct of Research . U.S. Department of Health and Human Services . Washington, DC: Government Printing Office [ Google Scholar ]
• Stephan P. (2012a). How Economics Shapes Science . Boston, MA: Harvard University Press [ Google Scholar ]
• Stephan P. (2012b). Research efficiency: Perverse incentives . Nature . 484 , 29. [ PubMed ] [ Google Scholar ]
• Stern A.M., Casadevall A., Steen R.G., and Fang F.C. (2014). Financial costs and personal consequences of research misconduct resulting in retracted publications . eLife . 3 , e02956. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stroebe W., Postmes T., and Spears R. (2012). Scientific misconduct and the myth of self-correction in science . Perspect. Psychol. Sci. 7 , 670. [ PubMed ] [ Google Scholar ]
• Swazey J.P., Anderson M.S., Lewis K.S., and Louis K.S. (1993). Ethical problems in academic research . Am. Scientist. 81 , 542 [ Google Scholar ]
• The Economist. (2010, Dec. 16). The Disposable Academic: Why doing a PhD is often a waste of time . Available at: www.economist.com/node/17723223 (accessed September16, 2016)
• The Economist. (2013, Oct. 19). How Science Goes Wrong . Available at: www.economist.com/news/leaders/21588069-scientific-research-has-changed-world-now-it-needs-change-itself-how-science-goes-wrong (accessed September16, 2016)
• The New York Times. (1991, Jun. 4). Commencements; Science Official Tells M.I.T. of Perils to Ethics. Available at: www.nytimes.com/1991/06/04/us/commencements-science-official-tells-mit-of-peril-to-ethics.html (accessed September16, 2016)
• Thoman D.B., Brown E.R., Mason A.Z., Harmsen A.G., and Smith J.L. (2014). The role of altruistic values in motivating underrepresented minority students for biomedicine . BioScience . 65 , 183 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tierney J. (2013, Sep. 10). Your Annual Reminder to Ignore the U.S. News & World Report College Rankings . The Atlantic . Available at: www.theatlantic.com/education/archive/2013/09/your-annual-reminder-to-ignore-the-em-us-news-world-report-em-college-rankings/279103 (accessed September16, 2016)
• Tijdink J.K., Verbeke R., and Smulders Y.M. (2014). Publication pressure and scientific misconduct in medical scientists . J. Empir. Res. Hum. Res. Ethics. 9 , 64. [ PubMed ] [ Google Scholar ]
• U.S. Anti-Doping Agency (U.S. ADA). (2012). Lance Armstrong Receives Lifetime Ban and Disqualification of Competitive Results for Doping Violations Stemming from His Involvement in the United States Postal Service Pro-Cycling Team Doping Conspiracy . Available at: www.usada.org/lance-armstrong-receives-lifetime-ban-and-disqualification-of-competitive-results-for-doping-violations-stemming-from-his-involvement-in-the-united-states-postal-service-pro-cycling-team-doping-conspi (accessed September16, 2016)
• U.S. Centers for Disease Control and Prevention. (2004). Blood lead levels in residents of homes with elevated lead in tap water–District of Columbia, 2004 . CDC Morbid. Mortal. Weekly Rep. 53 , 268 [ PubMed ] [ Google Scholar ]
• U.S. Cong. House, Committee on Oversight and Government Reform. (2015). EPA Mismanagement II July 29, 2015. Hearing. 114th Congress. 1st Session Available at: https://oversight.house.gov/hearing/epa-mismanagement-part-ii (accessed September16, 2016) [ Google Scholar ]
• U.S. Cong. House, Committee on Oversight and Government Reform. (2016). Examining Federal Administration of the Safe Drinking Water Act in Flint , Michigan Hearing, February 3, 2016. Hearing. 114th Congress. 2nd Session Available at: https://oversight.house.gov/hearing/examining-federal-administration-of-the-safe-drinking-water-act-in-flint-michigan (accessed September16, 2016) [ Google Scholar ]
• U.S. Department of Health and Human Services. (2013). Office of Research Integrity Annual Report 2012
• U.S. House Committee on Education and the Workforce. (2014). The Just In Time Professor . Available at: http://democrats.edworkforce.house.gov/sites/democrats.edworkforce.house.gov/files/documents/1.24.14-AdjunctEforumReport.pdf (accessed September16, 2016)
• U.S. House Committee on Science and Technology. (2010). A Public Health Tragedy: How Flawed CDC Data and Faulty Assumptions Endangered Children's Health in the Nation's Capital . Report by the Majority Staff on the Subcommittee on Investigations and Oversight. Available at: http://archives.democrats.science.house.gov/publications/caucus_detail.aspx?NewsID=2843 (accessed September16, 2016)
• Van Noorden R. (2010). Metrics: A profusion of measures . Nature . 465 , 864. [ PubMed ] [ Google Scholar ]
• Van Noorden R. (2014). Publishers withdraw more than 120 gibberish papers . Nature . Available at: www.nature.com/news/publishers-withdraw-more-than-120-gibberish-papers-1.14763 (accessed September16, 2016)
• Werner R. (2015). The focus on bibliometrics makes papers less useful . Nature . 517 , 245. [ PubMed ] [ Google Scholar ]
• Wilson W.A. (2016, May). Scientific regress . First Things . Available at: www.firstthings.com/article/2016/05/scientific-regress (accessed September16, 2016)
• Wolverton M. (1998). Treading the tenure-track tightrope: Finding balance between research excellence and quality teaching . Innovative Higher Educ . 23 , 61 [ Google Scholar ]
• Zetter K. (2009, Feb. 6). TED: Barry Schwartz and the Importance of Practical Wisdom . WIRED . Available at: www.wired.com/2009/02/ted-barry-schwa (accessed September16, 2016)
• Zhivotovsky L., and Krutovsky K. (2008). Self-citation can inflate h-index . Scientometrics . 77 , 373 [ Google Scholar ]

Research and Publication: Importance in the 21st Century

• First Online: 03 March 2021

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• Amar Ranjan 2 ,
• Arshi Rizwan 3 ,
• Lawanya Ranjan 4 ,
• Harshita Dubey 5 &
• M. D. Ray 6  

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Through ages, India has been one of the first nations to start Medical research through Ayurveda but with the need of hour, Research gradually demanded more proof and reasons so as to know the mechanism and treatment of disease. With demanding times, India with the rest of the world started escalating in research based on more scientific proofs and know how; although it is still at par with the western world. Because of which, the Government of India is promoting Research & Development on a large scale. Earlier, research was thought to be associated with the domain of masters and PhD (Doctor of Philosophy) programs only, but with increasing need and for career progression, it is now being extended amongst individuals at faculty post along with their medical practice. Keeping in mind India still being a developing country and more than 13 billion people to supervise, medical aid is still the priority in majority parts of the country and research being the second. It is only the top Institutes of the country that offer strategic Research & Development. However, now that the imminence of Research is well known, we all must faith the need of educating its significance in children since childhood, and keeping that in mind there are strategies to incur R&D programs as early as a part of undergraduate study curriculum. Multiple organizations like Indian Council of Medical Research, Department of Science and Technology, Dept. Of Biotechnology, University Grants Commission, etc., in India are promoting such programs. Hitherto Research is also one of the highest paid services in our country. The importance of Research and publication is described below in brief.

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Macilwain C. Science economics: what science is really worth. Nature. 2010 Jun;465(7299):682a.

Article   Google Scholar  

Budget of the United States Government: Analytical perspectives – United States. Office of Management and Budget – Google Books [internet]. [cited 2020 Jul].

Google Scholar  

Stokes DE. Pasteur’s Quadrant: Basic Science and Technological innovation. Washington, DC: Brookings Institution Press; 2011. p. 204.

Definition of Research. Western Sydney University [internet]. [cited 2020 Jul]. Available from: https://www.westernsydney.edu.au/research/researchers/preparing_a grant application/dest definition of research

Statistics c=AU; o=Commonwealth of Aou=Australian B of. Chapter – Chapter 2. Type of Activity classification. [Internet]. C=AU; o=Commonwealth of Australia; ou=Australian Bureau of Statistics. [cited 2020 Jul 4]. (1998). Available from: https://www.abs.gov.au/ausstats/abs%.nsf/66f306f503e529a5ca25697e00 1766 I f/22E4C I 84CA 29CA25697E0018FD78’? Opensource.

Serdyukov P. Innovation in education: what works, what doesn’t, and what to do about it’? Res lnnox’ Teach Learn. 2017;10(1):4–33.

Maddux CD, Liu L. Publishing Research Findings: Some Suggestions for Junior Faculty.

Pinar WF. Curriculum studies in India. New York: Palgrave Macmillan; 2015.

Book   Google Scholar  

Grant I, Brutscher P-B, Kirk SE, Butler L, Wooding S. Capturing research impacts: a review of international practice. Santa Monica, CA: RAND.

Research funding. Measuring the results of science investments – PubMed [internet]. [cited 10 Jul 2020]. Available from https://pubmed.ncbi.nlrn.nih.gov/21310987/

Wooding S, Hanney S, Buxton M, Grant J. Payback arising from research funding: evaluation of the Arthritis Research Campaign. Rheumatology. 44(9):1145–56. https://doi.org/10.1093/rheumatology/keh708 .

Mapping the impact: exploring the payback of arthritis research. RAND [internet]. [cited 10 Jul 2020]. Available from https://www.rand.org/pubs/monographs/MG862.html

Sea lt for the L of the. Yearbook 1998. Martinus Nijhoff Publishers; 2000. 164 p.

Swales JM. Genre analysis: English in academic and research settings. Cambridge: Cambridge University Press; 1990.

Gitanjali B. Identifying a research topic: the problem is the problem. Indian Pharmacol [internet]. [cited Jul 2020]. Available from: http://www.ijp-online.com/article.asp’? Issnw253-7613; year=2005; v’olu me=37; issue=2; spage=67; epage=68; aulast=Gitanjali

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Ranjan, A., Rizwan, A., Ranjan, L., Dubey, H., Ray, M.D. (2021). Research and Publication: Importance in the 21st Century. In: Ray, M.D. (eds) Multidisciplinary Approach to Surgical Oncology Patients. Springer, Singapore. https://doi.org/10.1007/978-981-15-7699-7_15

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Importance of Research in Education

8 Pages Posted: 19 Nov 2020

Mayurakshi Basu

National Council of Educational Research and Training

Date Written: October 2, 2020

Research is a scientific and systematic investigation or inquiry especially through search for new facts in any branch of knowledge. On the other hand education is regarded as the aggregate of all the processes by which a person develops abilities, attitudes and other forms of behavior of practical values in the society in which she or he lives. The core purpose of this paper is to understand the importance of research in education. Research is widely regarded as providing benefits to individuals and to local, regional, national, and international community’s involved in the education system. The thrust areas of this paper are characteristics, purposes of research in education, steps involved in research, importance of research in education and lastly challenges of research in present context.

Keywords: Research Importance Challenges Education

JEL Classification: I

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National Council of Educational Research and Trai Regional Institute of Education Bhubaneswar, OR 751022 751022 (Fax)

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Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century (2012)

Chapter: 3 importance of deeper learning and 21st century skills.

3 Importance of Deeper Learning and 21st Century Skills

This chapter summarizes research on the importance of deeper learning and “21st century skills” to success in education, work, and other areas of adult responsibility. The first section focuses on educational achievement and attainment, the second section on work, the third on health and relationship skills, and the fourth on civic participation. Overall, the research reviewed in these sections finds statistically significant, positive relationships of modest size between various cognitive, intrapersonal, and interpersonal competencies and desirable adult outcomes. However, these relationships are based on correlational research methods.

We also reviewed evidence on the role of formal schooling in adult success, which we include in the sections on work and health. We found statistically significant, positive relationships between years of educational attainment and labor market success, not only in research using correlational methods, but also in studies using stronger research methods (see discussion below). Measured cognitive, intrapersonal, or interpersonal competencies appeared to account for surprisingly little of these relationships between years of educational attainment and labor market success. In the fifth section, we show that the benefits of additional years of formal schooling for individuals include not only higher wages but also somewhat greater adaptability to changes in workplace technology and in jobs.

The literature discussed in this chapter comes from a variety of disciplines, including industrial-organizational psychology, developmental psychology, human resource development, and economics. Researchers in these disciplines have investigated the relationship between a range of different skills and abilities and later outcomes, using a variety of methods and data

sets. Some of the evidence we present is correlational in nature, and we call these “simple correlations.” Other evidence is longitudinal, in which competencies and other capacities measured at one point are related to outcomes measured years later, often after adjusting for individuals’ differences in family backgrounds. We call these “adjusted correlations” and view this evidence as more suggestive of causal connections than the evidence from simple correlations, but still prone to biases from a variety of sources. The strongest causal evidence, particularly the evidence of the impacts of years of completed schooling on adult outcomes, comes from statistical methods that are designed to approximate experiments.

IMPORTANCE TO EDUCATIONAL SUCCESS

Many more studies of school success have focused on the role of general cognitive ability (IQ) than specific interpersonal and intrapersonal competencies (see Table 3-1 ). Economists tend to lump all competencies other than IQ into the category of “noncognitive skills.” Personality and developmental psychologists have developed a much more refined taxonomy of them.

Most personality psychologists have centered their work on the “big five” personality traits—conscientiousness, openness, agreeableness, emotional stability, and extroversion—plus general cognitive ability. Although these traits have traditionally been viewed as relatively stable across the life span, a growing body of evidence indicates that that personality traits change in response to general life experiences (e.g., Roberts, Walton, and Viechtbauer, 2006; Almlund et al., 2011) and to structured interventions (see Chapters 4 and 5 ).

Developmental psychologists have a dynamic view of competence and behavioral development, with children’s competencies and behaviors determined by the interplay between their innate abilities and dispositions and the quality of their early experiences (National Research Council, 2000). Both groups have investigated associations among cognitive, intrapersonal, and interpersonal competencies and children’s success in school.

Personality Factors and School Success

The comprehensive Almlund et al. (2011) study of personality and attainment offers the following summary of “prediction” evidence on correlations and, in some cases, adjusted correlations between personality traits and educational attainment (see also Table 3-1 ):

Measures of personality predict a range of educational outcomes. Of the Big Five, Conscientiousness best predicts overall attainment and achieve-

ment. Other traits, such as Openness to Experience, predict finer measures of educational attainment, such as attendance and course difficulty. Traits related to Neuroticism also affect educational attainment, but the relationship is not always monotonic. Conscientiousness predicts college grades to the same degree that SAT scores do. Personality measures predict performance on achievement tests and, to a lesser degree, performance on intelligence tests. (p. 127)

It is important to note that while these associations are large enough to pass conventional thresholds of statistical significance, they almost never account for more than a nominal amount of the variation in the educational attainment outcomes under study.

The most noteworthy meta-analysis of these kinds of data is by Poropat (2009), who examined studies of the simple correlations between personality factors and school grades in primary, secondary, and higher education. 1 He found a significant positive association between conscientiousness and grades in primary school through college (see top half of Table 3-2 ). The simple correlations between conscientiousness and grades in high school and college were in the 0.20-0.25 range, about as high as the correlations between measures of general cognitive ability and grades in high school and college. 2 In comparison with other correlates of grades identified in previous studies, these two correlations are at approximately the same level as socioeconomic status (Sirin, 2005) and slightly lower than the correlations found for conscientiousness in industry training programs (Arthur et al., 2003).

In elementary school, general cognitive ability is the strongest correlate of grades, although all five personality factors are positively correlated with grades. Correlations between personality factors and grades generally fall over the course of high school and college. In higher education, among the five personality factors, only conscientiousness is correlated with grades.

Three studies of the correlations between “big five” personality traits and completed schooling have included at least some regression controls (Goldberg et al., 1998; van Eijck and de Graaf, 2004; Almlund et al., 2011). All find positive adjusted associations for concientiousness that range from 0.05 to 0.18, and all find modest negative adjusted associations for extroversion, agreeableness, and neuroticism.

___________________

1 The Poropat (2009) analysis included many more studies focused on grades in secondary (24-35 studies) and higher education (75-92 studies) than in elementary school (8 studies).

2 In social science research, such correlations are generally interpreted following rules of thumb developed by Cohen (1988), in which a correlation of 0.20 is considered small, a correlation of 0.50 is considered medium, and a correlation of 0.80 is considered large.

TABLE 3-1 Key Studies Cited in Chapter 3 : The Importance of Deeper Learning and 21st Century Skills

SOURCE: Created by the committee.

TABLE 3-2 Correlations and Regression-Adjusted Associations Among Skills, Behaviors, and School Performance

NOTE: Concurrent correlations for personality factors and cognitive ability come from Poropat (2009). Concurrent correlations for skills and behaviors in kindergarten and fifth grade come from Duncan and Magnuson (2011). Longitudinal and regression-adjusted correlations are from Duncan et al. (2007). Regression controls in the final column include family background, child temperament, and IQ. SOURCE: Created by the committee.

Skills, Behaviors, and School Success

There are many ways that developmental psychologists classify competencies in the cognitive, intrapersonal, and interpersonal domains, and some of their categories correspond to some of the “big five” personality traits. One recent review classified important competencies into four groups: achievement, attention, behavior problems, and mental health (Duncan and Magnuson, 2011).

Achievement, in the cognitive domain, refers to concrete academic competencies such as literacy (e.g., for kindergarteners, decoding skills such as beginning to associate sounds with letters at the beginning and end of words) and basic mathematics (e.g., ability to recognize numbers and shapes and to compare relative sizes). Although scores on tests of cognitive ability and achievement tend to have substantial correlations, there is an important conceptual difference between cognitive ability as a relatively stable trait and the concrete achievement competencies that develop in response to schooling and other environmental inputs.

Attention, in the intrapersonal domain, refers to the ability to control impulses and focus on tasks (e.g., Raver, 2004). Developmental psychologists often distinguish between two broad dimensions of behavior problems that reflect the domains of interpersonal and intrapersonal competencies—externalizing and internalizing. Externalizing behavior refers to a cluster of related behaviors, including antisocial behavior, conduct disorders, and more general aggression (Moffitt, 1993; Campbell, Shaw, and Gilliom, 2000). Internalizing behavior refers to a similarly broad set of mental health constructs, including anxiety and depression as well as somatic complaints and withdrawn behavior (Bongers et al. ,2003). 3

Many studies have established simple and, in some cases, adjusted correlations between this set of intrapersonal and interpersonal competencies and academic outcomes in the early grades (e.g., Vitaro et al., 2005, and Currie and Stabile, 2007, for attention; Pianta and Stuhlman, 2004, for antisocial behavior; and Fantuzzo et al., 2003, for depressive symptoms). Duncan and Magnuson (2011) use nationally representative data on kindergarteners and fifth graders to compute the simple correlations shown in the bottom left panel of Table 3-2 . Since letter grades are rarely recorded in the early grades, the table shows correlations between reading achievement and measures of attention, antisocial behavior and mental health. All are substantial by fifth grade, with the expected positive achievement

3 Cutting across the attention and externalizing categories is the idea of self-regulation, which current theory and research often subdivides into separate cognitive (cool) and emotional components (hot) (Raver, 2004; Eisenberg et al., 2005; Raver et al., 2005). Cognitive self-regulation fits into our “attention” category while emotional self-regulation fits into our “behavior problems” category.

associations for attention and negative associations for antisocial behavior and mental health problems. All of these associations are smaller in kindergarten, which, in contrast with the research on personality factors (Poropat, 2009), suggests increasing correlations as children grow older.

Averaging across six longitudinal data sets, Duncan et al. (2007) calculate the bivariate correlations shown in the “longitudinal correlations” column of Table 3-2 . Shown here are simple correlations among kindergarten entry achievement, attention and behavioral competencies, and math and reading test scores measured 2-8 years later. Correlations between later achievement and the three measures of attention, antisocial behavior, and mental health problems are similar to what was found for corresponding correlations with kindergarten achievement shown in the first column. As might be expected, correlations between math and reading competencies at school entry and later in the elementary school years are quite high.

To more accurately assess the importance of any one of these competencies and behaviors for school and career success, some studies have gone beyond these simple correlations to account for the fact that children with different levels of a given competency or behavior are likely to differ in many other ways as well. Children with, say, higher math scores may also have higher IQs, be better readers, exhibit less antisocial behavior, or come from more advantaged families. When adjustments for differences in these other conditions are made, the size of the relationship between early competencies and behaviors and later outcomes tends to shrink. This is shown in the fifth and sixth columns of numbers in Table 3-2 . A clear conclusion from these columns of numbers is that only three of the five school-entry competencies have noteworthy adjusted correlations with subsequent reading and math achievement: reading, math, and attention. Neither behavior problems nor mental health problems demonstrated a statistically significant positive correlation with later achievement, once achievement and child and family characteristics are held constant. 4

Studies estimating bivariate correlations between high school completion and measures of early competencies and behaviors—including achievement, attention, behavior problems, and mental health—find them to be quite modest (.05 to .10; Entwisle, Alexander, and Olson, 2005; Duncan and Magnuson, 2011, Appendix Table 3.A9). Even when these competencies and behaviors are measured at age 14, none of the correlations with high school completion is stronger than .20.

Much larger correlations are observed for early indications that children have persistent deficits in some of these competencies and behaviors. In particular, children with persistently low mathematics achievement and

4 A replication and extension analysis by Grissmer et al. (2010) also found predictive power for measures of fine motor skills.

persistently high levels of antisocial behavior across elementary school were 10-13 percentage points less likely to graduate high school and about 25 percentage points less likely to attend college than children who never have these problems (Duncan and Magnuson, 2011). In contrast, persistent reading and attention problems had very low adjusted correlations with these attainment outcomes. 5

IMPORTANCE TO WORKPLACE SUCCESS

Technological advances, globalization, and other changes have fueled demand for more highly educated workers over the past four decades. Across much of the 1980s, the inflation-adjusted earnings of high school graduates plunged by 16 percent, while the earnings of college-educated workers rose by nearly 10 percent. In the following two decades, low-skill worker earnings continued to fall, while the earnings of college-educated workers continued their modest rise. 6

How these occupation and education-related changes in the labor market affect the demand for cognitive, intrapersonal, and interpersonal competencies is the subject of this section. We begin with a brief review of the large literature on the economic payoff to years of formal education, and of the remarkably modest extent to which prior cognitive, intrapersonal, and interpersonal skills account for that payoff. We then turn to a more detailed discussion of trends in demand for 21st century competencies.

Educational Attainment and Employment Outcomes

From the pioneering work in the 1960s and 1970s of Schultz (1961), Becker (1964), and Mincer (1974) to the present, studies have shown that investments in education produce rates of monetary return that are comparable or higher than market rates on investment in physical capital. Remarkable in this literature is that the estimates have changed little as increasingly sophisticated studies have eliminated likely sources of bias in the estimation of the economic payoff to education, the most prominent of which is the self-selection of more able or motivated into higher levels of completed schooling. 7

5 These results come from an analysis in which the predictive power of any given skill or behavior was assessed after adjusting for the others and for family background characteristics.

6 Autor, Katz, and Kearney (2008, Table 1). Data are based on weekly earnings for full-time workers with 5 years of experience. Earnings of high school dropouts fell even more than the earnings of high school graduates (see also Levy and Murnane, 2004).

7 An overview of the efforts to address these bias issues is provided in Card (1999). One strategy for reducing bias from genetic factors is to use siblings or even identical twins to relate earnings and employment differences to schooling differences pairs of otherwise ¨similar¨

In most studies, the so-called private rate of return to added years of schooling (which relates the after-tax earnings benefits enjoyed by workers to the portion of the education costs they have borne) for the United States has varied between 7 and 11 percent, with even higher rates in many other countries (Psacharoupoulos and Patrinos, 2004). The social rate of return tends to be lower than the private rate of return because it includes the full resource costs of schooling provision, much of which is paid through government subsidies rather than the students themselves.

Barrow and Rouse (2005) have concluded that each additional year of schooling generates additional income of about 10 percent, a return that is about the same across the races. And Autor, Katz, and Kearney (2008, Figure 2A) estimate that the earnings advantage for college as opposed to high school graduates rose from about 50 percent higher in the mid-1970s to close to twice as high in 2005. In their summary of evidence on education curriculum, Altonji, Blom, and Maghir (2012) find greater labor market returns to more advanced high school courses and to engineering, business, and science majors in college.

Looking beyond earnings, Oreopoulos and Salvanes (2011) find that workers with higher educational attainment enjoy more nonmonetary employment advantages, including a higher sense of achievement, work in more prestigious occupations, and greater job satisfaction than comparable workers with lower levels of education. Those with more formal education are more likely to be selected for jobs that require further training and that merit training investment. Presumably, the rationale for basing selection decisions on the candidate’s level of education is that the costs of training for reaching job proficiency are reduced when more educated persons are chosen for training programs (Thurow, 1975; Lynch, 1994). Finally, evidence suggests that one person’s added years of schooling benefits others by raising the productivity of other workers at all levels of education (Moretti, 2004). 8

In short, the economic importance of a highly educated workforce is impressive and, if anything, increasing. Since the schooling process

individuals. For example, using Norwegian data, Oreopoulos and Salvanes (2011) find that, in comparison with their siblings, siblings with 1 additional year of education have annual incomes that are about 5 percent higher and lower probabilities of being unemployed or on welfare. Another is to use instrumental variable strategies based on, for example, compulsory schooling laws, where the obligatory age of school attendance determines the number of years and the permissible date at which students can leave. Since years of schooling under the compulsory attendance requirements are not subject to voluntary choice, differences in education are exogenous to other influences that might affect the amount of education obtained. None of these strategies is free from all potential biases, however.

8 Using a different estimation strategy that focuses only on the returns to secondary schooling for individuals subject to compulsory school attendance laws, Acemoglu and Angrist produce a smaller, but still positive, estimate of external returns than Moretti (2004).

presumably imparts the competencies and behaviors that are responsible for these productivity advantages, it is important to know how cognitive, intrapersonal, and interpersonal competencies are connected to education’s high rates of return.

Test Scores, Education, and Employment Outcomes

Cognitive competencies (as measured by standardized test scores) have the potential to play an important role in accounting for the links between schooling and earnings. First, since smarter people are more likely to acquire more schooling, failure to control for differences in prior cognitive competencies may bias estimates of the role of education per se . But second, even if two graduating high school seniors with identical cognitive competencies make different decisions about whether to attend college, the college experience itself might develop capabilities that command higher earnings from employers.

Surprisingly, empirical studies show that cognitive competencies are able to account for only a small fraction of the association between education and earning. Bowles, Gintis, and Osborne (2001) summarized 25 studies conducted over four decades, which yielded 58 estimates of earnings functions that incorporated test scores. They found that the estimated effect of schooling on earnings retained about 82 percent of its value, on average, after accounting for prior test scores, suggesting that most of the impact of years of educational attainment on earnings was attributable to determinants other than the cognitive competencies.

A second, more direct, approach to investigating the role of cognitive competencies on labor market outcomes does not involve the intervening role played by schooling. An extensive literature, including meta-analyses (e.g., Schmidt and Hunter, 1998, 2004) has examined the simple, unadjusted correlations between cognitive ability, personality factors, and job performance. Schmidt and Hunter (2004) reviewed several studies and meta-analyses, finding that measures of general cognitive ability were strongly correlated (the magnitude of these correlations was higher than 0.53) with occupational level, income, job performance, and job training performance. Comparing these correlations with those found in studies of the association between personality traits and job outcomes, they concluded that general cognitive ability was more important for later job success than conscientiousness or any other intrapersonal or interpersonal competency.

It is worth noting that an NRC committee (1989) reanalyzed the data from over 700 criterion-related studies of the concurrent correlations between scores on a test of general cognitive ability and measures of job performance (typically supervisor ratings, but in some cases, grades in a training course) in about 500 jobs. They found that, despite claims of

much higher predictive validities (i.e., correlations) in the literature (U.S. Department of Labor, 1983), the average correlation in studies that had been conducted since 1972 was about .25 after correction for sampling error. Cognitive test scores explained about 6 percent of the variance in performance, leaving 94 percent to be explained by other factors. Estimates of predictive validities in one subsequent review of the empirical literature also reflected this modest range (Sackett et al., 2001).

Economists have favored prospective longitudinal studies of the relationship between cognitive competencies and earnings (Hanushek and Woessman, 2008). In their examination of the associations between earnings and the cognitive skills of 15-18-year-olds as measured by the Armed Forces Qualifying Test, Neal and Johnson (1996) found that, with no controls for family background, a one-standard deviation increase in test scores was associated with roughly a 20 percent increase in earnings for both men and women. Using data from the National Child Development Survey (NCDS), which has followed a cohort of British children born in 1958 through midlife, Currie and Thomas (1999) related scores on reading and math tests administered at age 7 to wages and employment at age 33. Even in the presence of extensive family background controls, their models show 10-20 percent earnings differentials when comparing both males and females in the top and bottom quartiles of the two test score distributions. Murnane, Willett, and Levy (1995) show links between the mathematics tests scores of two cohorts of high school seniors and their wages at age 24.

Intrapersonal and Interpersonal Competencies and Employment Outcomes

In an effort to understand the large amount of variation in earnings and other employment outcomes that cannot be attributed to cognitive competencies, researchers have begun to examine the role of a variety of intrapersonal and interpersonal competencies. As with our earlier review of the determinants of achievement and attainment, research divides into a focus on personality factors and on other competencies and behaviors.

Personality Factors

Almlund et al. (2011) summarize their review of correlational evidence on the role of “big five” personality traits for labor market outcomes as follows:

Personality measures also predict a variety of labor market outcomes. Of the Big Five traits, Conscientiousness best predicts overall job performance but is less predictive than measures of intelligence. Conscientiousness,

however, predicts performance and wages across a broad range of occupational categories, whereas the predictive power of measures of intelligence decreases with job complexity. Additionally, traits related to Neuroticism (e.g. locus of control and self-esteem) predict a variety of labor market outcomes, including job search effort. Many traits predict sorting into occupations, consistent with the economic models of comparative advantage…. Personality traits are valued differentially across occupations. (p. 127)

A key study in this literature is Barrick, Mount, and Judge (2001), which conducts a second-order meta-analysis of the results of 11 prior meta-analyses of the simple associations between Five Factor Model personality traits and job performance. They find that conscientiousness is a valid correlate of job performance across all performance measures studied, with average correlations ranging from the mid .20s to low .30s. Emotional stability was correlated with overall work performance although not with all of the work performance criteria examined. The remaining factors—extroversion, openness and agreeableness—failed to correlate consistently with overall work performance.

Skills, Behaviors, and Earnings

The literature on links between earnings and specific achievement and behavioral skills has employed prospective longitudinal data and well-controlled regression models, yielding stronger evidence than that provided by studies of simple correlations. For example, Heckman, Stixrud, and Urzua (2006), using data from the National Longitudinal Study of Youth (NLSY) estimate substantial adjusted correlations between earnings and a scale combining adolescent self-esteem and sense of personal effectiveness.

Carneiro, Crawford, and Goodman (2007) use data from the British NCDS to relate a wide variety of achievement and behavioral measures assessed when the sample children were 11 years old to later earnings. The diversity of their behavioral measures is reflected in their names: “anxiety for acceptance,” “hostility toward adults,” “withdrawal,” and “restlessness.” When summed into a single index, a standard deviation increase in this collection of antisocial skills and behaviors is found to be associated (net of parental background) with a 3.3 percent decrease in age-42 earnings, about one-fifth of the estimated positive association for a one standard-deviation increase in achievement tests scores. Ironically, an examination of the social and behavioral subscales found the greatest explanatory power for “inconsequential behavior”—a heterogeneous mixture of items related to inattention (“too restless to remember for long”), antisocial behavior (“in informal play starts off with others in scrapping and rough play”), and inconsistency (“sometimes eager, sometimes doesn’t bother”).

In more recent work, Cunha and Heckman (2008) used longitudinal data to study cognitive and noncognitive development over time as it affects high school completion and earnings. They developed a battery of noncognitive scores focused on an antisocial construct using student anxiety, headstrongness, hyperactivity, and peer conflict to go along with cognitive test scores in this analysis. Based upon the psychological, neurological, social, and other aspects of child development, they modeled the developmental path and estimated the impact of investments in cognitive and noncognitive competencies on high school graduation and earnings (at age 23) at three different periods during the age span from 6 to 13. The parental investments studied included purchases of books and musical instruments, newspaper subscriptions, special lessons, trips to the museum, and trips to the theater.

The authors found that the impact of investment returns shifts markedly as the child ages, from cognitive competencies at the earlier ages (6 and 7 to 8 and 9) to noncognitive competencies during the later period (9-13). They also found evidence that noncognitive outcomes contribute to cognitive test results, but little evidence that test scores affect noncognitive outcomes. This finding suggests that investments in noncognitive competencies may contribute to economic productivity not only directly but also by increasing cognitive achievement.

One difficulty in research evaluating and comparing the relative associations between labor market outcomes and both cognitive and noncognitive competencies is the lack of strong measures of noncognitive competencies. Cognitive competencies are measured using well-established and validated standardized testing methods. By contrast, noncognitive competencies are almost always measured by ratings rather than tests—either self-ratings or ratings by observers who are not experts.

Better measurement methods, for example, by trained psychologist observers, might result in more valid measurement and therefore an increase in the estimated importance of noncognitive competencies. This apparently is the finding of a study by Lindqvist and Vestman (2011), which analyzed data on military enlistees in Sweden, where enlistment is compulsory for male 18-year-olds. These individuals complete a cognitive ability test and an extensive questionnaire. A trained psychologist combined the latter with results from a 30-minute clinical interview to assess the individual’s noncognitive competencies, particularly, responsibility, independence, outgoingness, persistence, emotional stability, and initiative. The researchers examined a Swedish database and were able to match labor market outcomes of 14,703 32- to 41-year-olds who had earlier been tested through the enlistment. Comparing the impact of cognitive and noncognitive measures on wages, unemployment, and annual earnings, they found that, in general, the adjusted correlations between these outcomes and their noncognitive

variable were larger than the correlations of earnings with their cognitive variable. Men who did poorly in the labor market were especially likely to lack noncognitive abilities. In contrast, cognitive ability was a stronger correlate of wages and earnings for workers with earnings above the median.

But while this body of research on intrapersonal and interpersonal competencies is growing rapidly, there is little consensus emerging from it. The prospective studies reviewed above capitalize on the haphazard availability of measures in their data sets. Much further investment is needed to specify such competencies and measure them in a streamlined way. Such specification will be useful in understanding how best to teach noncognitive skills to students (Durlak and Weissberg 2011; see Chapter 6 ) and how mastery of such competencies may, in turn, affect employment, earnings, and other adult outcomes. The European Commission has begun to examine how noncognitive competencies and personality traits contribute to workplace success (Brunello and Schlotter, 2010).

Trends in Demand for 21st Century Competencies

Clearly, labor market demand for increased years of schooling has risen over the past four decades. There is also some evidence that employers currently value and reward a poorly identified mix of cognitive, intrapersonal and interpersonal competencies. As noted in previous chapters, the committee views 21st century skills as dimensions of human competence that have been valuable for many centuries, rather than skills that are suddenly new, unique, and valuable today. One change from the past may lie in society’s desire that all students now attain levels of mastery—across multiple areas of skill and knowledge—that were previously unnecessary for individual success in education and the workplace. Another change may lie in the pervasive spread of digital technologies to communicate and share information. Although the underlying communications and information-processing competencies have not changed, they are applied at an increasing pace to accomplish tasks across various life contexts, including the home, school, workplace, and social networks. According to recent press reports, over half of the estimated 845 million Facebook users around the globe log on daily; among those aged 18 to 34, nearly half check Facebook within minutes of waking up and 28 percent do so before getting out of bed (Marche, 2012). An estimated 400 million people use Twitter to send or receive brief messages. Even in the world of print media, the pace of communication has quickened, as newspapers adopt a “digital first” strategy and publish fresh information online as news stories break (Zuckerman, 2012). Here, we review research addressing the question of whether such changes are increasing demand for cognitive, intrapersonal, and interpersonal competencies, and, if so, whether this will continue in the future.

The economy’s need for different kinds of worker competencies has shifted over time due to a variety of factors, including shifts in the distribution of occupations. Blue collar jobs have shrunk dramatically over the past 40 years, declining from nearly one-third of all jobs in 1979 to only one-fifth of all jobs in 2009. Over the same time period, white collar administrative support jobs, such as filing clerks and secretaries, also declined. This rapid decline in middle-skill, middle-wage jobs has been accompanied by rapid growth at the top and bottom of the labor market, with a trend toward increasing polarization in wages and educational requirements (Autor, Katz, and Kearney, 2008).

The growth jobs at the top and bottom of the labor market is illustrated by Bureau of Labor Statistics (BLS) data, which organizes all occupations in 10 large clusters, three of which—professional/related, service, and sales—constitute fully half of the labor force. The two largest clusters—professional/related (e.g., computer science, education, healthcare professions) and service (e.g., janitorial, food service, nursing aids, home healthcare workers)—are at the opposite ends of the spectrum in terms of education and wages. These two clusters are projected to create more new jobs than all of the other 8 occupational clusters combined over the period 2008 to 2018 (Lacey and Wright, 2009).

Autor, Levy, and Murnane (2003) conducted a study that analyzed not only the mix of occupations but also the competencies demanded within occupations. Drawing on the Dictionary of Occupational Titles (a large catalogue of occupations and their characteristics), they developed measures of the routine and nonroutine cognitive tasks and routine and nonroutine manual tasks required by various occupations. Comparing tasks over time, from 1960 to 1998, they concluded that beginning in 1970 computers reduced routine cognitive and manual tasks and increased nonroutine cognitive and interactive tasks. Their model explained 60 percent of the growth in demand for college-educated labor over the period from 1970-1988. The authors concluded that computers substitute for workers in performing routine tasks and complement workers in performing nonroutine tasks.

Building on this study, Levy and Murnane (2004) argued that demand is growing for expert thinking (nonroutine problem solving) and complex communication competencies (nonroutine interactive skills). Levy and Murnane (2004) also proposed, that demand is growing for verbal and quantitative literacy. They view reading, writing, and mathematics as essential enabling competencies that supported individuals in mastering tasks that require expert thinking and complex communication production processes. Predicting that jobs requiring low or moderate levels of competence will continue to decline in the future, the authors recommended that schools teach complex communication and nonroutine problem-solving competencies, along with verbal and quantitative literacy, to all students.

More recently, Autor, Katz, and Kearney (2008) analyzed data on wages and education levels from 1962 to 2005. The analysis supports the argument that computers complement workers in performing abstract tasks (nonroutine cognitive tasks) and substitute for workers performing routine tasks. However, it also suggests that the continued growth of low-wage service jobs can be explained by computers’ lack of impact on nonroutine manual tasks. Noting that these tasks, performed in service jobs such as health aides, security guards, cleaners, and restaurant servers, require interpersonal and environmental adaptability that has proven difficult to computerize, Autor, Katz, and Kearney (2008) suggest that low-wage service work may grow as a share of the labor market.

Goos, Manning, and Salomons (2009) reached a similar conclusion, based on an analysis of occupational and wage data in Europe. They concluded that technology was the primary cause of polarization in European labor markets, eliminating routine tasks concentrated in mid-level manufacturing and clerical work while complementing nonroutine tasks in both high-wage professional jobs and low-wage service jobs.

These two studies both suggest that low-wage service work involves nonroutine tasks that cannot be readily replaced by computers. There is debate in the literature about the level of cognitive, intrapersonal, and interpersonal competencies required to perform such work. Some case studies and surveys suggest that successful performance in low-wage service jobs requires complex communications skills and nonroutine problem solving (Gatta, Boushey, and Appelbaum, 2007). However, the low levels of education required to enter these jobs, together with their low wages and a plentiful supply of unskilled labor, suggests that their competency demands are—and will remain—low (Autor, 2007). Yet another view is that the competencies required by these and other jobs depend largely on management decisions about how the job is structured and the level and type of training provided (National Research Council, 2008).

Borghans, ter Weel, and Weinberg (2008) studied the role of interpersonal competencies in the labor market and concluded that “people skills” are an important determinant of occupations and wages. They argue that interpersonal competencies vary both with personality and across occupations, and that individuals are most productive in jobs that match their personality. They also found evidence that youth sociability affects job assignment in adulthood, and that interpersonal interactions are consistent with the assignment model. This study built on earlier, unpublished work which suggested that technological and organizational changes have increased the importance of interpersonal competencies in the workplace (Borghans, ter Weel, and Weinberg, 2005).

While these studies propose that demand for cognitive, intrapersonal, and interpersonal competencies has grown in recent decades and will

continue to grow in the future, some experts disagree. For example, Bowles, Gintis, and Osborne (2001) analyzed longitudinal studies that presented 65 different correlational estimates of the relationship between cognitive test scores and earnings over a 30-year period. The authors found no increase in the estimates over time, indicating that labor market demand for cognitive competencies had not grown. Based on responses to a new national survey of skills, technology, and management practices, Handel (2010) argues that, for most jobs in the U.S. economy, education and academic skill demands are low to moderate, noting that large numbers of workers report educational attainments that exceed the requirements of their jobs.

All efforts to predict future competency demands are, of necessity, based on past trends. For example, BLS has often been criticized for using past trends to project detailed occupational requirements and competency needs a decade into the future (National Research Council, 2000). Similarly, Levy and Murnane (2004) call for schools to teach complex communications skills and nonroutine problem solving based on the assumption that the trends identified by Autor, Levy, and Murnane (2003) will continue for decades.

IMPORTANCE TO HEALTH AND RELATIONSHIP SKILLS

Education, Competencies, and Health Outcomes

There is a long history of research on the associations between education and health. Researchers statistically analyze data from self-reports on health status, behavior, and challenges in terms of explanatory variables, including gender, race, age, education, and income. Based on these analyses, they construct a health gradient demonstrating the conditional relation between education and health status. The overwhelming finding is that general health status, specific health outcomes, and healthy behaviors are strongly and positively correlated with educational attainment.

Cutler and Lleras-Muney (2010a) summarized the literature in which educational attainment is linked both statistically and substantively to health outcomes and behaviors. They found higher levels of educational attainment were associated with an array of reductions in adverse health events and increases in healthy eating and exercise. For example, the age-adjusted mortality rate of high school dropouts was found to be about twice that of those with some college in the 25-64-year-old age group in 1999.

Although these findings are widely accepted, two important questions dominate the literature. The first is to what degree is this relation causal as opposed to the explanation that those with better health are more likely to succeed educationally? That is, to what degree is the coefficient or gradient for health by level of educational attainment biased upward by

reverse causation or omitted determinants of both education and health. The second question refers to the mechanism by which education improves health results. While the simplest explanation is that more educated persons are more knowledgeable about how to improve and maintain their health status and are better able to respond to health problems, there are other explanations. These include the effects of education on access to the healthcare system (for example, through higher income) or effects of education on increasing consideration for the long-run consequences of present behavior and taking preventative measures.

To answer the first question, health economists have relied increasingly on the use of instrumental variables techniques to isolate the exogenous effects of education on health outcomes. Following the studies on education and labor market outcomes, they have used externally imposed differences in compulsory schooling such as changes in compulsory attendance requirements that affect the amount of education attained. To control for genetic factors and family backgrounds, they have also compared the health of siblings who have different educational attainments. Lochner (2011) provides a recent review of the latest set of studies employing these sophisticated methodologies. His preferred set of 39 estimates shows a wide range of estimates of education effects on mortality, self-reported health, and disability, as well as two health-related behaviors—smoking and obesity. Not all of the estimates are statistically significant, and some have the wrong signs. By and large, the links tend to be stronger in U.S. than European studies.

With respect to trying to isolate the mechanisms by which education influences health outcomes and behavior, the relations are less clear. There is some evidence that both the general cognitive capabilities of more educated persons as well as specific knowledge contributes to this relation. Cutler and Lleras-Muney (2010b) have also attempted to decompose the education-health nexus into major components including differences associated with education, socioeconomic status and income, and access to social networks. They find that about 30 percent of the education-health gradient is due to a combination of the advantages of income, health insurance, and family background associated with more education; 10 percent is due to the advantages of social networks; and about 30 percent is due directly to education. They also explore the educational mechanisms that might account for the relationship. They conclude that it may not be the specific health knowledge conferred by education as much as greater interest and trust of science and general skills such as critical thinking and decision-making abilities, analytic abilities, and information processing skills that enable educated individuals to make better health-related decisions. Such mechanisms as risk aversion and longer-range time considerations (low time discount rate) do not seem to have substantial support in explaining the health gradients.

A few studies have attempted to estimate links between health and cognitive, intrapersonal, and interpersonal competencies. The Almlund et al. (2011) review reaches the following conclusions regarding personality traits:

All Big Five traits predict some health outcomes. Conscientiousness, however, is the most predictive and can better predict longevity than does intelligence or background. Personality measures predict health both through the channel of education and by improving health-related behavior, such as smoking. (pp. 127-128)

Many of these conclusions are based on the meta-analysis of Roberts et al. (2007), who review evidence from 34 different studies on links between longevity and the “big five” personality traits. They find that conscientiousness was the strongest predictor among the “big five” traits and a stronger predictor than either IQ or socioeconomic status. openness to experience and agreeableness were also associated with longevity, while neuroticism was associated with shorter life spans.

Among individual studies, Conti, Heckman, and Urzua (2010a, 2010b) estimate a multifactor model of schooling, earnings, and health outcomes using data from the British Cohort Study. They find that cognitive ability is not a very important determinant of smoking decisions or obesity but that noncognitive competencies are generally more important for smoking, obesity, and self-reported health. More recently, Hauser and Palloni (2011) studied the relationship between high school class ranking, cognitive ability, and mortality in a large sample of American high school graduates. They found that the relationship between cognitive ability (IQ) and survival was entirely explained by a measure of cumulative academic performance (rank in high school class) that was only moderately associated with IQ. Moreover, the effect of class ranking on survival was three times greater than that of IQ. The authors’ interpretation of these findings is that higher cognitive ability improves the chances of survival by encouraging responsible, well-organized, timely behaviors appropriate to the situation—both in terms of high school academics and in later-life health behaviors.

COMPETENCIES AND HEALTHY RELATIONSHIPS IN ADULTHOOD

Insights into the importance of transferable competencies for healthy marriages and other relationships in adulthood can be gleaned from the literature in a number of areas. Our review concentrates on three: (1) studies of couple satisfaction and marriage duration, (2) programs designed to promote healthy marriages, and (3) programs targeting teen relationship building.

A literature review by Halford et al. (2003; see also Gonzaga, Campos, and Bradbury, 2007) suggests four broad classes of variables that impact the trajectory of relationship satisfaction over time: couple interaction, life events impinging upon the couple, enduring individual characteristics of the partners, and contextual variables. Most relevant to the committee charge are the enduring individual characteristics and interactions.

Behavioral genetic studies show substantial heritabilities for divorce in adulthood (McGue and Lykken, 1992; Jockin, McGue, and Lykken, 1996). A handful of studies have examined early childhood correlates of adult relationship stability. Two of the most relevant drew data from the Dunedon birth cohort study. Newman et al. (1997) found that undercontrolled temperament observed at age 3 predicted greater levels of conflict in romantic relationships at age 21. Relatedly, Moffitt et al. (2011) found that childhood self-control predicts the likelihood of being a single parent.

Most personality traits are not very predictive of relationship satisfaction (e.g., Gottman, 1994; Karney and Bradbury, 1995). However, low neuroticism (i.e., high ability to regulate negative affect) as an adult has been found to predict high relationship satisfaction (Karney and Bradbury, 1997). In addition, Davila and Bradbury (2001) find that low anxiety over abandonment and comfort with emotional closeness are also predictive.

Among the elements of couple interaction, effective communication competencies has predicted relationship satisfaction in numerous studies although, interestingly enough, prospectively and not concurrently (Karney and Bradbury, 1995).

Insights into needed skills can also be gleaned from the curricula of effective adult couple relationship education programs. Many such programs attempt to boost couples’ positive communication, conflict management, and positive expressions of affection (Halford et al., 2003). In contrast, curricula for teen relationship programs promote positive attitudes and beliefs rather than skills, although, as with adult programs, some also target relationship behavior (Karney et al., 2007).

IMPORTANCE TO CIVIC PARTICIPATION

Civic engagement is variously understood to include involvement in activities focused on improving one’s community, involvement in electoral activities (voting, working on campaigns, etc.), and efforts to exercise voice and opinion (e.g., protests, writing to elected officials, etc.) (Zukin et al., 2006). Academics, foundations, and policy makers have expressed concern about decreasing levels of political engagement in the United States, particularly among youth. For example, political scientist Robert Putnam (2000) drew attention to Americans’ lack of connection through clubs, civic associations, and other groups in his influential book Bowling Alone .

In response to these concerns, there has been a resurgence of interest in the development of the knowledge, skills, and dispositions that facilitate civic engagement—this cluster of knowledge, skills, and dispositions is sometimes referred to as “civic literacy.” Studies are looking at the roles played by peers, schools, the media, and other factors in civic literacy and engagement (Delli Carpini and Keeter, 1997; Niemi and Junn, 1998). A recent review of this literature (Garcia Bedolla, 2010) finds that schools have a greater impact on civic literacy than was previously thought, and it has also pointed to the importance of parents and neighborhoods. However, these studies have focused on young people’s attitudes, dispositions, or intentions about future political behavior, and have not linked school-based civics programs with later voting behavior and other civic activities in adulthood.

Prevalence of Civic Participation

Recent survey data suggest that some forms of engagement are fairly widespread (e.g., voting in general elections, volunteerism, consumer boycotts). A majority of young people report that they regularly follow public affairs (Lopez et al., 2006). But upward of 60 percent of young people are unable to describe activities that they can attribute to civic or political engagement, and a significant percentage is “highly disengaged.” These young people do not generally believe their civic or political actions are likely to make much difference. Another type of civic participation is direct political action—protest, work on political campaigns, and the like. Overall, just 13 percent of young people are reported as being intensely involved in politics at this level—survey data indicate they are motivated by a desire to address a social or political problem.

Factors Associated with Civic Participation

Studies have shed light on the factors that correlate with political engagement, focusing on the role of family, schools, and peers in the development of children’s political attitudes and behaviors. Early studies found that families tend to be more important than schools, as political orientations and other attitudes and perspectives appeared to be socially inherited from parents to children (Abramowitz, 1983; Achen, 2002). Indeed, research over four decades has demonstrated that socioeconomic status (SES) is a strong predictor of engagement and participation (Garcia Bedolla, 2010). More recent studies underscore the importance of parents and neighborhoods in the socialization process; they also indicate that schools can play a more important role than was previously believed (Niemi and Junn, 1998; Kahne and Sporte, 2008).

The literature linking years of schooling with civic outcomes is extensive. However, as with labor market and health outcomes, studies providing convincing causal estimates are relatively rare. Lochner (2011) provides a review of these rigorous studies and concludes that this literature suggests important effects of completed schooling on a wide range of political behaviors in the United States, but not in the United Kingdom or Germany. The U.S. impacts are found for voting registration and behavior, political interest, and the acquisition of political information.

Smith (1999) examined the effects of early investments in young people’s social capital on political involvement and “civic virtue” in young adulthood. Using longitudinal data, she examined parental involvement, youth religious involvement, and participation in voluntary associations. She found that early extensive connections to others, close family relationships, and participation in religious activities and extracurricular activities during adolescence were significant predictors of greater political and civic involvement in young adulthood.

EDUCATIONAL ATTAINMENT AND TRANSFER IN THE LABOR MARKET

A general theme of the evidence presented in this chapter is that measurable cognitive competencies, personality traits, and other intrapersonal and interpersonal competencies developed in childhood and adolescence are, at best, modestly predictive of adult successes, particularly labor market productivity. Cognitive ability does appear to matter and, among personality traits, so, apparently, does conscientiousness. But, in the research to date, their predictive power is modest. In terms of “transfer,” we are unable to point to a particular set of competencies or behaviors that have been shown to transfer well to the labor market. (Boosting these skills may increase educational attainment, however, as discussed in the following chapters.)

Education attainment, in contrast, is strongly predictive of labor market success, even in research approaches designed to approximate random assignment experiments. Measurable cognitive, intrapersonal, and interpersonal competencies account for surprisingly little of the impact of education on future productivity. But even if we do not know exactly what it is about spending an additional year in school that makes people more productive, a policy approach designed to promote attainment might be promising, particularly if it can be shown that attainment promotes competencies that are transferable across jobs or across an individual’s entire career.

Prior to the human capital revolution of the 1960s, the manpower planning approach assumed that each job and occupation required a specific level and type of education. Education policy planners produced projections

of economic output by sector multiplied by a fixed formula of occupational requirements per unit of output that was further translated into a rigid formula of educational needs of a future labor force. Needless to say, the manpower forecasts failed, largely because of the rigid assumptions relating educational requirements to occupation and occupational requirements to economic output. Changes in technology, organization, and the market prices of labor and capital, and error-prone projections of sectoral output all undermined the accuracy of the projections of educational need. 9

Becker’s (1964) early work on human capital took a more general approach by distinguishing between general and specific human capital. He proposed that education developed “general” human capital that was valuable across different firms, while training and experience within a firm work developed “specific” human capital, valuable only in a particular firm. Becker’s (1964) human capital model depended upon market dynamics in which adjustments would take place through responses to the costs and productivity of different kinds of labor. Labor supply and demand were expected to adapt, as any changes in demand for human capital resulting from changes in the firm’s organization, technology, and mix of outputs would be met by individual and company investments in education, job training, and on-the-job learning.

There is considerable evidence that labor supply, allocation, and productivity are widely adaptable to changes in the economy, especially over the long run. This is because education increases the capacity of workers to learn on the job, benefit from further training, and respond to productive needs as they arise. Workers with more education are generally able to learn their jobs more quickly and do them more proficiently. They can work more intelligently and with greater precision and can accomplish more within the same time period. Greater levels of education increase their ability to benefit from training for more complex job situations, and this is evidenced in the literature on training. 10 The research demonstrating the overall impact of education on productivity and economic outcomes did not address precisely what competencies were developed by educational investments. However, an important insight was established by Nelson and Phelps (1966), who suggested that a major contribution of education was to enable workers to adapt to technological change.

Welch (1970) and Schultz (1975) generalized this insight to suggest that investments in more educated workers had an even greater impact on a firm’s ability to adapt to technological change. They argued that hiring more educated workers can improve a firm’s productivity not only because, relative to less educated workers, these workers are more productive in

9 See Blaug (1975) for a trenchant critique of this type of approach.

10 See Lynch (1992); Leuven and Oosterbeek (1997); Blundell et al. (1999).

their current jobs and can be more quickly and easily trained for complex jobs, but also because they can allocate their time and other resources more efficiently in their own jobs and in related jobs in ways that increase the overall productivity of the firm. In this way, the contributions of more educated workers go beyond their own job performance to impact the overall performance of the organization. For both Welch and Schultz, these benefits represent the greatest opportunity for investments in more educated workers to pay off for the firm.

More education, and higher education in particular, appears to develop workers’ abilities to master an understanding of the production process and to tacitly make adjustments to changes in prices, technology, the productivity of inputs, or mix of outputs. These continuous adjustments allow the firm to “return to equilibrium” (in economic terms), maximizing productivities and profits. Neither Welch nor Schultz addressed which specific aspects of schooling contributed to the ability of workers to make the tacit adjustments to production that will increase productivity and profitability. It is possible that schooling develops not only cognitive competencies but also intrapersonal and interpersonal competencies that enable workers to make decisions that benefit the firm.

Welch (1970) and Schultz (1975) provide many examples of how investments in more educated workers may help firms adjust to optimize their productivity and profits, but there are also many examples of adjustments to disequilibria in the overall labor market. During the Second World War, women replaced males in the labor force in what had been male occupations, continuing the high rates of productivity needed to support both the war effort and the economy (Goldin, 1991). Chung (1990) studied vocationally trained workers for particular occupations who had been employed in those occupations or in occupations that were not matched specifically to their training. He found that workers who had received vocational training for a declining manufacturing industry, textiles, were substantially switching to a growing and thriving manufacturing industry, electronics, and were receiving considerably higher earnings in the latter than in the former. That is, the supply of workers was adapting in the short run to the changes in demand, and in the longer run the occupational training choice of workers was adapting too.

The historical evidence suggests that education is transferable across occupations because many occupations require common skills. For example, Gathmann and Schonberg (2010) found that competencies developed at work (which Becker viewed as “specific” and not valuable outside the firm) were more portable than previously thought. Analyzing data on the complete job histories and wages of over 100,000 German workers, along with detailed information on the tasks used in different occupations, they found that workers developed task-specific knowledge and skills and were

rewarded accordingly, with higher wages as they gained experience in an occupation. On average, workers who changed occupations—whether voluntarily or because they were laid off—were more likely to move to an occupation requiring similar tasks (and attendant competencies) to their previous occupation than to a “distant” occupation requiring very different competencies. Laid-off workers who were unable to find work in similar occupations and were forced to move to a distant occupation experienced higher wage losses than those who were able to find work in similar occupations.

The authors found that university graduates appeared to gain more task-specific knowledge and skills than less educated workers and to be rewarded accordingly with higher wages. However, when more highly educated workers were required to move to distant occupations, their wages declined more than did the wages of less highly educated workers who had to move to a distant occupation. This suggests that the deep task-specific competencies developed by the highly educated workers were less transferable than the shallower competencies developed by the less educated workers. Overall, the study suggests that workers are more easily able to transfer competencies developed on the job to a similar occupation, involving similar tasks, than to a dissimilar occupation. This is analogous to research findings from the learning sciences, which have found that transfer of learning to a new task or problem is facilitated when the new task or problem has similar elements to the learned task (see Chapter 4 ).

Other evidence suggests that even workers with relatively lower levels of education may be able to adapt to the demands of complex jobs. One measure of adaptability is the substitutability among workers with different levels of education. Economists measure employers’ ability to substitute workers at one level of education for jobs that normally are associated with a higher level of education by examining how the mix of more and less educated workers changes as relative wages for different educational levels change. Historical studies in the United States suggest that each 10 percent increase in the labor costs of a higher level of education is associated with a 15 percent decrease in employment at that educational level and increase in workers with less education to replace them (Ciccone and Peri, 2005). This implies that employers view workers as highly adaptable to perform jobs that traditionally require more education, when relative wages encourage such substitution.

CONCLUSIONS AND RECOMMENDATIONS

The research evidence related to the relationship between various cognitive, intrapersonal, and interpersonal competencies is limited and uneven in quality. Some of the evidence reviewed in this chapter is correlational

in nature and should be considered, at best, suggestive of possible causal linkages. Other evidence, from longitudinal studies, is more suggestive of causal connections than the correlational evidence, but it is still prone to biases from a variety of sources. The strongest causal evidence, particularly the evidence of the impacts of years of completed schooling on adult outcomes, comes from statistical methods that are designed to approximate experiments.

• Conclusion: The available research evidence is limited and primarily correlational in nature; to date, only a few studies have demonstrated a causal relationship between one or more 21st century competencies and adult outcomes. The research has examined a wide range of different competencies that are not always clearly defined or distinguished from related competencies.

Many more studies of the relationships between various competencies and outcomes (in education, the labor market, health, and other domains) have focused on the role of general cognitive ability (IQ) than on specific intrapersonal and interpersonal skills (see Table 3-1 ). Economists who conduct such studies tend to lump all competencies other than IQ into the category of “noncognitive skills,” while personality and developmental psychologists have developed a much more refined taxonomy of them. All three groups have investigated the relationships between cognitive, intrapersonal, and interpersonal competencies and outcomes in adolescence and adulthood.

• Conclusion: Cognitive competencies have been more extensively studied than intrapersonal and interpersonal competencies, showing consistent, positive correlations (of modest size) with desirable educational, career, and health outcomes. Early academic competencies are also positively correlated with these outcomes.
• Conclusion: Among intrapersonal and interpersonal competencies, conscientiousness (staying organized, responsible, and hardworking) is most highly correlated with desirable outcomes in education and the workplace. Antisocial behavior, which has both intrapersonal and interpersonal dimensions, is negatively correlated with these outcomes.

Across the available studies, the relative size of the correlations with the three different domains of skills is mixed. There is some evidence that better measurement of noncognitive competencies might result in a higher estimate of their importance in education and in the workplace.

A general theme of the evidence presented in this chapter is that measurable cognitive skills, personality traits, and other intrapersonal and interpersonal competencies developed in childhood and adolescence are, at best, modestly predictive of adult successes, particularly in the labor market. Educational attainment, in contrast, is strongly predictive of labor market success, even in research approaches designed to approximate random assignment experiments. Measurable cognitive, intrapersonal, and interpersonal competencies account for surprisingly little of the impact of education on future wages (wages, in economic theory, reflect productivity).

Studies by economists have found that more highly educated workers are more productive than those with less years of schooling are because more highly educated workers are better able to accomplish a given set of work tasks and are also more able to benefit from training for more complex tasks. In addition, more highly educated workers have the capacity to allocate resources more efficiently in their own work activities and in behalf of the enterprise in which they work than do workers with fewer years of schooling.

• Conclusion: Educational attainment—the number of years a person spends in school—strongly predicts adult earnings, and also predicts health and civic engagement. Moreover, individuals with higher levels of education appear to gain more knowledge and skills on the job than do those with lower levels of education and they are able, to some extent, to transfer what they learn across occupations. Since it is not known what mixture of cognitive, intrapersonal, and interpersonal competencies accounts for the labor market benefits of additional schooling, promoting educational attainment itself may constitute a useful complementary strategy for developing 21st century competencies.

The limited and uneven quality of the research reviewed in this chapter limits our understanding of the relationships between various cognitive, intrapersonal, and interpersonal competencies and adult outcomes.

• Recommendation 1: Foundations and federal agencies should support further research designed to increase our understanding of the relationships between 21st century competencies and successful adult outcomes. To provide stronger causal evidence about such relationships, the programs of research should move beyond simple correlational studies to include more longitudinal studies with controls for differences in individuals’ family backgrounds and more studies using statistical methods that are designed to approximate

experiments. Such research would benefit from efforts to achieve common definitions of 21st century competencies and an associated set of activities designed to produce valid and reliable assessments of the various individual competencies.

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Americans have long recognized that investments in public education contribute to the common good, enhancing national prosperity and supporting stable families, neighborhoods, and communities. Education is even more critical today, in the face of economic, environmental, and social challenges. Today's children can meet future challenges if their schooling and informal learning activities prepare them for adult roles as citizens, employees, managers, parents, volunteers, and entrepreneurs. To achieve their full potential as adults, young people need to develop a range of skills and knowledge that facilitate mastery and application of English, mathematics, and other school subjects. At the same time, business and political leaders are increasingly asking schools to develop skills such as problem solving, critical thinking, communication, collaboration, and self-management - often referred to as "21st century skills."

Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century describes this important set of key skills that increase deeper learning, college and career readiness, student-centered learning, and higher order thinking. These labels include both cognitive and non-cognitive skills- such as critical thinking, problem solving, collaboration, effective communication, motivation, persistence, and learning to learn. 21st century skills also include creativity, innovation, and ethics that are important to later success and may be developed in formal or informal learning environments.

This report also describes how these skills relate to each other and to more traditional academic skills and content in the key disciplines of reading, mathematics, and science. Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century summarizes the findings of the research that investigates the importance of such skills to success in education, work, and other areas of adult responsibility and that demonstrates the importance of developing these skills in K-16 education. In this report, features related to learning these skills are identified, which include teacher professional development, curriculum, assessment, after-school and out-of-school programs, and informal learning centers such as exhibits and museums.

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Article Contents

Model organisms, people power, conclusions, acknowledgments, references cited.

• < Previous

Empowering 21st Century Biology

Gene E. Robinson ( [email protected] ) is with the Department of Entomology, Neuroscience Program, and Institute for Genomic Biology, and Saurabh Sinha is with the Department of Computer Science, at the University of Illinois at Urbana-Champaign, in Urbana.

Jody A. Banks ( [email protected] ) is with the Department of Biology, and David E. Salt is with the Department of Horticulture and Landscape Architecture, at Purdue University, in West Lafayette, Indiana.

Dianna K. Padilla ( [email protected] ) is with the Department of Ecology and Evolution, at Stony Brook University, in Stony Brook, New York.

Warren W. Burggren is with Department of Biological Sciences at the University of North Texas, in Denton.

C. Sarah Cohen is with the Tiburon Center and Department of Biology, at San Francisco State University, in Tiburon, California.

Charles F. Delwiche is with the Department of Cell Biology and Molecular Genetics, at the University of Maryland, in College Park.

Vicki Funk is with the Department of Botany, at the Smithsonian Institution in Washington, DC.

Hopi E. Hoekstra is with the Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology, at Harvard University, in Cambridge, Massachusetts.

Erich D. Jarvis is with the Department of Neurobiology at Duke University, in Durham, North Carolina.

Loretta Johnson is with the Division of Biology at Kansas State University, in Manhattan, Kansas.

Mark Q. Martindale is with the Department of Zoology at the University of Hawaii, in Honolulu.

Carlos Martinez del Rio is with the Department of Zoology and Physiology at the University of Wyoming, in Laramie.

Monica Medina is with the School of Natural Sciences at the University of California, Merced.

Chelsea Specht is with the Department of Plant and Microbial Biology at the University of California, Berkeley.

Kevin Strange is with the Department of Anesthesiology at Vanderbilt University, in Nashville, Tennessee.

Joan E. Strassmann is with the Department of Ecology and Evolutionary Biology at Rice University, in Houston, Texas.

Billie J. Swalla is with the Department of Biology at the University of Washington, in Seattle.

Lars Tomanek is with the Department of Biological Sciences at California Polytechnic State University, in San Luis Obispo.

• Article contents
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• Supplementary Data

Gene E. Robinson, Jody A. Banks, Dianna K. Padilla, Warren W. Burggren, C. Sarah Cohen, Charles F. Delwiche, Vicki Funk, Hopi E. Hoekstra, Erich D. Jarvis, Loretta Johnson, Mark Q. Martindale, Carlos Martinez del Rio, Monica Medina, David E. Salt, Saurabh Sinha, Chelsea Specht, Kevin Strange, Joan E. Strassmann, Billie J. Swalla, Lars Tomanek, Empowering 21st Century Biology, BioScience , Volume 60, Issue 11, December 2010, Pages 923–930, https://doi.org/10.1525/bio.2010.60.11.8

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Several lists of grand challenges in biology have been published recently, highlighting the strong need to answer fundamental questions about how life evolves and is governed, and how to apply this knowledge to solve the pressing problems of our times. To succeed in addressing the challenges of 21st century biology, scientists need to generate, have access to, interpret, and archive more information than ever before. But for many important questions in biology, progress is stymied by a lack of essential tools. Discovering and developing necessary tools requires new technologies, applications of existing technologies, software, model organisms, and social structures. Such new social structures will promote tool building, tool sharing, research collaboration, and interdisciplinary training. Here we identify examples of the some of the most important needs for addressing critical questions in biology and making important advances in the near future.

Biology is confronted with the need to answer fundamental questions about how life and natural systems evolve, are governed, and respond to changing environments. We need to understand the basic biological processes that drive life on this planet—those common to all organisms as well as those that provide unique adaptation to different environments. We also urgently need to identify all the life forms on this planet and understand their interrelationships and geographic distributions.

Biology must also apply new and existing knowledge to solve the pressing problems of our times, which include the environmental crises of global climate change, ocean acidification, biodiversity loss and the introduction of nonnative species, serious concerns for human health, emerging and pandemic diseases, and critical needs for agricultural and biofuel production. The urgency of these fundamental and practical needs has prompted scientists to begin to identify sets of “grand challenges” in biology ( Denver et al. 2009 , NRC 2009 , Satterlie et al. 2009 , Schwenk et al. 2009 ).

To succeed in addressing the challenges of 21st century biology, scientists must generate, have access to, interpret, and archive more information than ever before. This effort will involve analyses that span scales of time and space, from decoding information from genomes to extracting information from the environment on how organisms survive and reproduce ( NRC 2009 ). Scientists need to learn how complex biological systems work across levels of organization, from cells to ecosystems, and across time scales, from the millisecond response of neural systems to the long-term response of evolutionary change. We need to be able to trace the effects of changes in DNA sequence or epigenetic regulation on multiple organismal phenotypes, understand how these changes affect ecological relationships, and have sufficient examples of these to begin to articulate new theories of organismal function and evolution. Addressing the challenges of 21st century biology requires integrating approaches and results across different subdisciplines of biology, such as genetics, development, physiology, ecology, and evolution, as well as technologies, information, and approaches from other disciplines, such as engineering, computer science, physics, chemistry, mathematics, and the geological and atmospheric sciences ( figure 1 ).

Tools for 21st century biology. To solve grand challenges, biology is becoming increasingly integrated across levels of organization, over different spatial and temporal scales, and it has become allied with other disciplines. Twenty-first century biology requires new tools that involve new technologies; new applications of existing technologies; new adaptations of tools from established model organisms to new models; new software; new model organisms; and new social structures to promote tool building, tool sharing, research collaboration, and interdisciplinary training.

However, biologists do not have the tools required to achieve this vision. For many important questions in biology, progress is stymied by a lack of the essential instruments to make rapid advances. In some cases, certain devices or technologies exist in other fields but are currently unavailable to biologists. In other cases, we need tools that scientists have not yet imagined. Developing those tools may require new technologies, applications of existing technologies, software, model organisms, and social structures to promote tool building, tool sharing, research collaboration, and interdisciplinary training. This article presents examples of what we believe to be the most important needs for tools to address critical questions in biology. We focus on the tools and the social structures needed to enable such tools; for an in-depth treatment of biology's grand challenges, see Denver and colleagues (2009) , the National Research Council report A New Biology for the 21st Century (2009), Satterlie and colleagues (2009) , and Schwenk and colleagues (2009) .

Researchers need tools to enable high-throughput acquisition and synthesis of information at all levels of the hierarchy of biological organization, and across all biologically relevant spatial and temporal scales. These include technologies, software, and devices related to “omics” informatics and systems biology; sensors and imaging; and information archiving.

Omics, informatics, and systems biology. The ability to sequence the genomes of microbes, plants, and animals has led to remarkable advances in biology. But this “first genomic revolution” has been based on the genome sequences of only a relatively small number of organisms: hundreds of microbes, and just a few dozen plant and animal species ( www.genomenewsnetwork.org/ ). The relentless push to lower DNA sequencing costs for biomedical purposes will continue, and will soon make it possible to sequence the genomes of most species of interest for any biological question. Lower sequencing costs will usher in a “second genomic revolution,” having a transformative effect on all areas of biology because genome sequence information can be used to illuminate questions at all levels of biological organization; we present just a few examples here.

DNA-based tools can have profound interdisciplinary impacts, beginning with faster and cheaper field identification of species and extending to assessments of genomewide patterns of genetic variation in different environments to determine what allows or limits the ability of individuals to adapt to changing environments. Planning for the sequencing of 10,000 different vertebrate species' genomes has already begun ( Haussler et al. 2009 ), and similar plans will certainly emerge for invertebrates and plants. In some cases, there also will be genome sequences for thousands of individuals belonging to the same species ( Kuehn 2008 , Weigel and Mott 2009 ). We envision many such projects for species that are important models in the laboratory or field, play particularly important ecological roles in different environments or that are situated at critical branch points in phylogeny.

Insights into the mechanisms and evolution of organisms can be gained with “ancient DNA” from specimens of archeological or historical importance ( Millar et al. 2008 ), and from specimens collected over centuries and held in museums or natural science collections. Ancient DNA can be used to study major evolutionary patterns and diversification, extinction, and temporal changes in genetic variation—studies that compare Neanderthal and Homo sapiens genomes, for example ( Green et al. 2006 ). Ancient DNA also could be used to understand molecular responses to past climate changes, and thus help predict potential responses in the future, but better tools are needed to facilitate the study of DNA. Improved DNA processing techniques that allow sequenceable DNA to be obtained from historic samples despite suboptimal preservation are needed to engender ever-more creative uses of ancient DNA in the future.

Metagenomics is revolutionizing the study of microbial ecology, from identifying new microbial species, strains, and genes to describing microbial communities associated with different parts of the human body. It is not far fetched to imagine the ability to extend this approach to eukaryotes, especially small ones. For example, strategically placed insect traps that feed into an automated metagenomic sequencing and informatics pipeline could be used to monitor outbreaks of agricultural pests or vectors of human disease.

New laboratory and computational tools also are required to leverage genome sequencing for a comprehensive omics revolution. Researchers need improved technologies for high-throughput interrogation of transcriptomes (including all forms of RNA transcripts) and novel methods for high-throughput in situ hybridization to precisely map changes in gene expression in order to illuminate our understanding of key biological processes.

To understand the complexity of biochemical processes in a living cell or organism, technologies to acquire comprehensive profiles of other molecules—such as metabolites (metabolomics), proteins (proteomics), elements (ionomics), and the stable isotopic composition of organisms—are just as important as genomic tools. The information obtained can be applied to a variety of problems, including the development of new drugs, improvements in human nutrition, and understanding the impacts of climate change.

Biologists need new bioinformatics methods to determine and compare genomes and functions across individuals and taxa, and to find and synthesize meaningful patterns within the floods of genomic data that will soon appear. New software should be easily accessible to biologists, and computer programs should not require researchers to have extensive programming skills to use large, integrated data sets of genomic, phenotypic, phylogenetic, ecological, and environmental information for in silico hypothesis testing and discovery. There is a need for cyberinfrastructure composed of databases, communication protocols, and computational services designed to help make data and computational tools more usable for biologists ( Giardine 2005 , Stein 2008 ). And underlying this new cyberinfrastructure must be ever-more powerful computers to provide accessible high-end computing.

The impending omics revolution will require that traditional comparative genomics methods, which have served well in studies with few genes or genomes, give way to new visualization and analytical approaches that simultaneously can use thousands of genome sequences. The availability of thousands of genome sequences should make it possible to devise new algorithms to better detect orthologs in distantly related species in order to study the evolution of complex traits, such as flowering, development, and social behavior, or to discover new mechanisms of biofuel production.

Contemporary software is needed to extract and synthesize genomic data so that DNA sequences become, in essence, a new repository of biological information. Imagine an environmental biologist in need of a model species to study the risks of a certain type of environmental toxicant posing the following question: What species are particularly vulnerable to this environmental toxicant? This question prompts the software to perform an automatic knowledge-based search to identify genes from the literature known to encode the relevant detoxifying enzyme, using powerful text-mining algorithms in development ( Muller et al. 2004 , Ling et al. 2007 ). If an answer were available, then the program would conduct a Web search of all genome sequences to find and report on those species that lack the corresponding gene or genes, or those individuals within a species with particular single-nucleotide polymorphisms. Not all the information returned from such a search would be useful; for example, there is a variety of technical reasons a gene may be missing from an assembly of a particular genome. But the ability to move seamlessly between genome content and higher-level biological questions will provide biologists with a novel and powerful means of extracting information from genomes.

Systems analyses at all levels of biological organization promise to provide an innovative framework for understanding complex traits, including those at the molecular level. This will help illuminate how variation in genotype is related to variation in phenotype. Scientists will need new bioinformatic tools to integrate omics data into sophisticated models of gene and protein regulatory networks. To study how organisms adapt to environmental change, bioinformatics will need to be able to integrate the information derived from regulatory, signaling, and metabolic networks ( Hyduke and Palsson 2010 ) with other types of phenotypic and population genetic data, ideally obtained across diverse environmental conditions. Automated methods of information analysis are sorely needed, as are user-friendly software programs that facilitate the integration of data from multiple levels of biological organization across spatial and temporal scales, and that lead to predictive models for specific conditions. Biologists must be able to navigate by moving easily up and down the biological hierarchy from micro- to macroscales. Investigators also need user-friendly modeling tools with algorithms that can infer causal relationships from large data sets to help them develop hypotheses.

Scientific databases and literature must be more interactive and dynamic. Improved forms of text mining, already in development, employ statistical literature analyses to help identify new biomarkers for human disease ( Shi et al. 2008 ) or explore how genes influence social behavior ( Ling et al. 2007 ).

Sensors. Progress in many areas of biology depends on learning how genotypes generate specific phenotypes, how these relationships vary with environmental conditions, and how these relationships have evolved ( Houle 2009 ). This requires new devices to measure organisms in their environment. Researchers need devices to enable acquisition of phenotypic and performance information that can be matched to genotypic and environmental information at all levels of the hierarchy of biological organization. Moreover, this information must be obtained under the vast array of natural and biological conditions in which organisms live and have evolved, and at biologically relevant spatial and temporal scales.

On a microscale, biologists need devices to continuously record the activity of cellular components as they interact naturally in living cells; on a macroscale, they need devices to continuously record the activity and performance of organisms and their component systems as they interact naturally in their environment. This instrumentation must be cost effective, miniaturized, and deployable in large numbers to continuously collect and transmit data in diverse environments, on small and large spatial scales. Automated image acquisition and shape-recognition software could permit the deployment of “smart” sensors that obtain information from specific organisms, both microscopic and macroscopic, and their environments, in real time. Stable isotopes already are being used as “natural sensors” because some enzymes (including rubisco and carbonic anhydrase) and biogeochemical processes (including temperature and precipitation) affect the isotopic profiles of organisms in characteristic ways. “Isoscapes,” made from isotope analysis coupled with geographic information systems (GIS), are starting to reveal the relative importance of key physical and biological processes at continental scales ( Bowen 2009 ). But isotopic analysis of biological materials is slow and labor intensive, and high-throughput methods are needed for this approach to realize its full potential. Advanced radio-tracking technologies will help with the study of dispersal and migration of small animals, such as bats, insects, and songbirds. It is expected that major innovations in radio-tracking technologies will make possible important new insights into conservation biology, climate change effects, and the spread of infectious disease ( Wikelski et al. 2007 ).

Earth's diverse environments also need to be more intensely monitored if scientists are to appropriately contextualize new knowledge about an organism's physiological and behavioral responses. Researchers require new technology to cheaply monitor and measure many environmental parameters, including pH, temperature, conductivity, wind force, water flow rates and directions, dissolved oxygen, and mineral nutrient content, at biologically relevant scales, under controlled conditions and in the field, in real time. This information will help answer fundamental questions in biology that relate to genotype-phenotype linkages; it also is critical to understanding and predicting anthropogenic effects on natural resources and the impacts of climate change. It is hoped that the National Ecological Observatory Network (NEON; www.neoninc.org ), funded by the US National Science Foundation (NSF), will provide large-scale terrestrial environmental data in the near future. Integrating NEON-type information with the above-mentioned measuring devices can then occur, enabling biologists to obtain information on individual organisms and to further study the impacts of climate change at all levels of biological organization.

Many forms of sophisticated sensory instrumentation and technology already exist, but they are not useful for discovering the linkages between genotypes, phenotypes, and the environment because of problems of scale. Many biological processes and environments are much smaller than current technology can measure. Miniaturization of instrumentation is critical for our understanding of basic life processes. Microfluidic devices are already starting to transform analyses of genomes, cells, and tissues in the laboratory ( Whitesides 2006 ). These devices need to be adapted for wider use under an array of conditions, including in natural environments. For example, “mini-mass spectrometers” already exist that can be placed below sea level to study the adaptation of diverse forms of life to extreme environmental conditions ( Bell et al. 2007 ). Further development of these technologies for a wide range of field-based applications could revolutionize real-time monitoring of organisms, populations, communities, and ecosystems.

Imaging. Biologists need handheld personal imaging systems that can be used or deployed in the field. Such imaging technologies would allow scientists to examine organisms in nature in detail, which is essential for making breakthroughs in fields such as sustainable agriculture, forestry, and conservation. These devices are now feasible, thanks to advances in real-time imaging. New methods, such as multilens cameras that allow post hoc adjustment of focus and depth of field and three-dimensional (3-D) image reconstruction ( Bimber 2006 ), permit imaging in ways that are qualitatively different from what can be done with commonly used instruments. Deconvolution imaging makes it possible to overcome the limits of numeric aperture to produce images with resolution or depth of field that exceeds what is possible with a single image ( Angel and Fugate 2000 ). Because these methods permit capture and integration of multiple traditional images into a single construct, they blur the line between image and computer model.

In microimaging, further development could make it possible to continuously monitor the 3-D structure of a developing organism, or to record the precise location and structure of every organism in an environment visible within the field of view. Raman spectroscopy is being used in the laboratory to provide detailed chemical analysis of specimens of ancient bone, shell, and teeth ( Freudiger et al. 2008 , Grant 2009 ). These and other imaging technologies need to be miniaturized, easy to use, portable, and cost effective. Such improvements would allow scientists to examine organisms in nature in detail, which is essential for making breakthroughs in fields such as sustainable agriculture, forestry, and conservation. It also would be possible to selectively sample organisms of interest and perform real-time, nondisruptive population monitoring, such as measuring the spread of invasive and pest species, including those that carry human disease. Discoveries could be made of the mechanisms that drive system and population resilience in the face of natural and anthropogenic disturbance or climate change.

In macroimaging, more user-friendly remote sensing and GIS would allow biologists to gain access to georeferenced data of all types. One widely used mapping tool is Google Maps, which has demonstrated the possibilities of using remote sensing and GIS technology to scientists for a variety of research purposes. Remote sensing and GIS technology can be improved in spatial resolution and image quality, and by the capability to integrate different types of data sets, including biological, geological, and topographical information ( Makris et al. 2009 ). These innovations would increase the effectiveness of spatial modeling and habitat prediction algorithms to more accurately predict the spread of invasive or pathogenic species and the consequences of land-use change, for example, on local or global ecosystem scales. Improved GIS modeling and mapping could also help scientists predict when and where a new disease might emerge by revealing places where human hosts and certain animal vector species are in close proximity and at high densities. Better remote sensing and GIS predictive tools are especially needed to study the more remote regions of the world.

Information archiving. Even now, our ability to acquire biological data far outstrips our ability to store it in reliable and easily retrievable formats. This is true for all types of data, from genome sequence information to archived museum specimens, to the wealth of environmental data being collected. Biologists need modern methods of archiving, sharing, and accessing data. These needs will become even more acute with biology poised to acquire unprecedented amount of data, including reference data and reference specimens. In addition, researchers need easy access to the information, online or in person.

There is a need for improved software tools for deployment in online databases, seed banks, stock centers, museums, and other repositories of biological information. To be most useful, these repositories need to be curated, and must be replete with and searchable by different types of information (e.g., for organism specimens, DNA, species, time, and place of collection). Such an update will require formalized ontologies for analytical data at all levels of biological organization ( Ashburner et al. 2000 ), and formalized methods of recording metadata that describe the analytical data. Integrating and maintaining older legacy data poses other sets of challenges in the digital era.

New information technology is required to facilitate database creation. This software includes programs to facilitate uploading newly acquired data to centralized storage locations and programs that automate the process of creating and maintaining community-specific databases, such FlyBase ( www.flybase.org ) or WormBase ( www.wormbase.org ), which historically have required extensive, and increasingly prohibitively expensive, manual cssuration.

To effectively use different types of data to address a common problem, researchers need new tools for data integration across databases with different formats or that reside in different locations. For example, with the profusion of genome sequences expected to come in the near future, it is likely that an increasing number of sequences will reside only on the computer servers of individual laboratories rather than in centralized repositories like GenBank ( www.ncbi.nlm.nih.gov/Genbank/ ), so it is imperative to develop software that can locate all the genome sequences in order to extract meaning from them. All of these needs again underscore the need for accessible high-end computing.

Over the past several decades, research efforts on plants and animals have increasingly focused on only a handful of model genetic organisms, especially thale cress ( Arabidopsis thaliana ), fruit fly ( Drosophila melanogaster ), worm ( Caenorhabditis elegans ), and mouse ( Mus musculus ). These species are especially useful for laboratory studies because they are relatively small bodied, have short generation times, can be maintained in the laboratory, and are comparatively easy to breed. An extensive array of genetic tools has been developed for these species, including finished genome sequences and advanced mutant and transgenic technology, such as transformation systems with control over spatial and temporal patterns of gene inactivation. However, these few species are not representative of the vast diversity of life. Twenty-first century biology would greatly benefit from a broader array of model organisms, including species not yet widely studied, to address a full range of important biological questions, especially those related to evolution and adaptation ( Abzhanov et al. 2008 , Brown et al. 2008 , Behringer et al. 2009 ). But many organisms used to study evolution and adaptation lack the genetic and genomic tools necessary for the most rapid progress in scientific research.

It is now necessary to expand the number of model species so that a broader collection of questions can be studied effectively and efficiently. To accomplish this goal, scientists must use new model species to their fullest potential, using methods that work for a variety of species.

New ways are needed to facilitate the development of forward and reverse genetic techniques for the analysis of gene function. Viral vectors have been used to overexpress genes in a few animal species ( Donaldson et al. 2008 ); innovations in viral modification could extend this technique more broadly. Gene knockdowns mediated by RNA interference (RNAi) are a powerful reverse genetics tool to analyze gene function, but this works better in some species and tissues than in others. The RNAi method also is limited by problems of delivery, knockdown efficiency, and artifacts resulting from off-target effects, and, in animals, innate immune responses. Enhancing the efficacy of RNAi across tissues and species, with methods that transfer easily across species, is an important goal. One critical component of empowering novel model organisms is the development of methods for genetic transformation that transfer easily from species to species. In animals, transposable elements such as PiggyBac show promise as vectors for transformation that can work on a broad variety of species, but additional research is required to build transformation systems that work efficiently for many species ( Wu et al. 2007 ).

Improved proteomic and metabolomic technologies make it possible to use species as models for environmental-change research even without fully sequenced and annotated genomes ( Epperson et al. 2004 , Tomanek 2010 ). These tools would empower new model organisms for physiological, developmental, behavioral, ecological, and evolutionary research.

A key issue is whether new tools should be developed on a species-specific basis, or whether they can be applied broadly across taxa. Species-specific tools will always require significant investment, and involve careful justification of which species to choose ( Mandoli and Olmstead 2000 , Jenner and Wills 2007 ). We suggest that emphasis be placed on species that can be used to understand key evolutionary patterns and important biological phenomena that present the most immediately pressing need. Additionally, because we anticipate that some of the most significant advances in biological research in this century will involve integration across levels of biological organization and across scales of time and space, we also suggest a special focus to empower new model organisms that allow for such integration. It is hoped that as new technologies are developed, costs will lower, facilitating further development of an ever-increasing number of model organisms. If these goals are achieved, the designation of “model” organism will become less relevant over time, as more and more species will be accessible to investigation at multiple levels of biological organization.

To make the most progress in 21st century biology, a scientific research culture that nurtures creativity, encourages and promotes tool building and sharing, and rewards scientists accordingly is essential. In addition, as biology becomes more and more interdisciplinary and information based, new training paradigms must prepare the next generation of biologists and tool builders. With the inter- and transdisciplinary development and use of tools comes the challenge of cross-disciplinary communication. This challenge is particularly vivid, for example, when computer scientists and biologists come together to develop and use bioinformatic tools. Bioinformatics must facilitate networking and the formation of virtual communities, resources for those who can “translate” across disciplines, and institutional mechanisms that recognize and reward the work and time needed to bridge communication across disciplines and transfer knowledge and technology.

Tool building, tool sharing, and collaboration. Tools with transformational potential have been, and will continue to be, developed by inspired and highly motivated individual scientists and engineers. We advocate the creation of more collaborative mechanisms to enhance and facilitate the process of tool building, and offer a few suggestions here.

Many of us lament the limited opportunities for interactions on our own campuses between biologists and those in other fields, such as engineering. Engineers are frequently unaware of biologists' needs, and biologists do not always know what technologies engineers have already produced or invented. Similarly, engineers are not always familiar with the solutions that biological systems provide for a variety of engineering problems. Workshops that bring together engineers and biologists serve as a catalyst for innovative tool development and could help remedy this issue.

National or regional facilities could serve as tool-development incubators or tool-dissemination sources. For example, a center that brings together engineers and biologists could play a crucial role in the design, fabrication, testing, and use of microfluidic devices for both the laboratory and the field. Another center, involving a different mix of researchers, including perhaps geneticists, developmental biologists, and physiologists, could be formed to develop universal techniques of transgenesis. In some cases, innovation could arise from breaking down communication barriers between fields so that problems can be clearly seen from different disciplinary perspectives.

Interdisciplinary centers might be in physical locations, connected with universities or independent research institutes that serve as focal points to bring people together for periods of time. Examples of highly successful centers include the NSF-sponsored National Center for Ecological Analysis and Synthesis and the National Evolutionary Synthesis Center (NESCent) ( Carpenter 2009 ). Field research stations and laboratories can be particularly effective venues for extended and informal cross-disciplinary interaction ( Carpenter 2009 ).

Virtual interdisciplinary centers with geographically dispersed partnerships will help make transformations in biology. Virtual centers could be particularly useful for the development of some of the new bioinformatic tools outlined above. Funding mechanisms that encourage the development of virtual communities to address particular biological problems have seen strong success; for example, the National Institutes of Health-funded Glue Grants ( www.nigms.nih.gov/Initiatives/Collaborative/GlueGrants/ ) , NSF-funded Research Coordination Networks ( www.nsf.gov/funding/pgm_summ.jsp?pims_id=11691 ), and the NSF iPlant initiative ( www.iplantcollaborative.org/ ). iPlant is specifically designed to address the development of cyberinfrastructure to facilitate solutions to grand challenges in the plant sciences. The nanoHUB (http://nanohub.org/) is a virtual center that distributes newly developed computational tools through easily navigated interfaces to users at all levels of computer sophistication. It is easy to imagine additional virtual centers developing around new model organisms, technologies, or ways of integrating across levels of biological organization.

One highly successful social structure in science is the research community. Unlike hard disciplinary boundaries, research communities are self-assembling and dynamic, and often cross delineations of study. Clearly, a sense of community and wanting to belong is not just a human characteristic but also a desirable motivating force in science. Biological communities are often structured around organisms (e.g., model organisms), systems (e.g., plant communities), disciplines (e.g., comparative physiologists, biomechanics, and functional morphologists), or fields of interest (evolutionary development). Perhaps tool development can be facilitated by creating new research communities. Will important new advances be made if research communities are organized around tools or major problems, rather than the organisms or systems they study? This is an experiment worth trying.

New forms of networking in science, fueled by innovations in communication technology that operate on increasingly short time scales, can also contribute to changes in social structure to facilitate 21st century biology. Scientists use a growing number of networking tools for research and public outreach, including Google, as well as Drupal.org, Epernicus, Facebook, LinkedIn, MyExperiment.com, SciVee.tv, Skype, Twitter, and YouTube. These social-networking tools are showing promise in facilitating just the kinds of social processes that 21st biology requires. They provide friendly and informal environments that can aid the sharing of both tools and data. For example, Epernicus is a social-networking Web site and professional networking platform resource built by scientists to help scientists find the right people with the right expertise at the right time. Networking tools should also prove useful in developing new research communities and supporting new training opportunities.

New institutional incentives are also needed to enable building and sharing tools for biology. Although some toolbuilders have been amply rewarded for their efforts, including with Nobel Prizes, the dominant motif in scientific research is discovery. The collective development of a new tool may not result in a publication in a high-impact journal, but a finding made with it might. Institutions need new ways of evaluating and recognizing collaborative efforts. Academic collaboration has long been valued in the abstract—most scientists recognize that the outcome of a successful collaboration is usually more than just the sum of individual parts, but traditional metrics of recognition favor individual achievement. Funding agencies and academic institutions have taken steps to incentivize scientists to form productive collaborative teams of researchers by establishing specific grants mechanisms for collaboration and creating interdisciplinary institutes, respectively. We expect these important paradigm-changing efforts to intensify in the future.

Training. Training paradigms are essential for preparing young biologists for the more extensive research collaboration that is needed for interdisciplinary work, and for providing them with the quantitative skills and broad perspectives necessary for success. Tomorrow's biologists must have training across genetics, development, biochemistry, physiology, ecology, and evolution, as well as experience working across different disciplines. More important, they also must have conceptual and quantitative training in mathematics and computer science to integrate these domains of knowledge using new computational tools. Wake (2008) emphasized the importance of training students early in their careers to not only think independently but also to work effectively in team-based scientific research.

Innovative training programs couple training with research in a team-based, problem-driven format. Undergraduate-focused universities provide opportunities for students to design and implement experiments and then analyze and present their results while receiving guidance and support from professors as well as peers. Innovative undergraduate training programs exist ( Pevzner and Shamir 2009 ), and some undergraduates already are annotating genomes and using mass spectrometers for environmental metabolomic projects, for example. Investing more in research-intensive training programs at a variety of undergraduate-focused universities will increase the size of a diverse and highly motivated graduate student pipeline.

This article identifies some tools that are critically needed for biology to answer fundamental questions about how life evolves and is governed, as well as tools to apply this knowledge to solve the pressing problems of our times. We have tried to highlight possibilities for tools that integrate and affect disciplines and those that allow scientists to work across levels of biological organization—these will likely have the strongest influence on 21st century biology. We have outlined steps necessary to create the culture and social and educational structures that will facilitate and nurture tool development and toolmakers now and in the future. Scientists require more than new technologies, devices, and software; they also need to create and support a culture of science and education that stimulates and nurtures creativity, supports potential toolmakers, and trains the next generation of engineers. Many tools not yet imagined might make possible the next revolutionary biological discoveries; they might enable scientists to study remote areas of the world or reach and integrate underserved and underrepresented groups in science, thus encouraging progress toward common societal values for human health and the natural environment.

We thank the National Science Foundation for supporting the workshop that led to this article, Letitia Cundiff for assistance in arranging the workshop and preparing the manuscript, and two anonymous reviewers for suggestions that improved the manuscript.

Abzhanov A Extavour CG Groover A Hodges SA Hoekstra HE Kramer EM Monteiro A . 2008 . Are we there yet? Tracking the development of new model systems . Trends in Genetics 24 : 353 – 360 .

Google Scholar

Angel R Fugate B . 2000 . Astronomy-adaptive optics . Science 288 : 455 – 456 .

Ashburner M Ball CA Blake JA Botstein D Butler H . 2000 . Gene ontology: Tool for the unification of biology . Nature Genetics 25 : 25 – 29 .

Bell RJ Short RT van Amerom FHW Byrne RH . 2007 . Calibration of an in situ membrane inlet mass spectrometer for measurements of dissolved gases and volatile organics in seawater . Environmental Science and Technology 41 : 8123 – 8128 .

Behringer R Johnson AD Krumlauf RE eds. 2009 . Emerging Model Organisms: A Laboratory Manual . Cold Spring Harbor Laboratory Press .

Google Preview

Bimber O . 2006 . Computational photography: The next big step . Computer 39 : 28 – 29 .

Bowen GJ et al.  . 2009 . Isoscapes to address large-scale Earth science challenges . Eos, Transactions of the American Geophysical Union 90 : 109 – 110 .

Brown FD Prendergast A Swalla BJ . 2008 . Man is but a worm: Chordate origins . Genesis 46 : 605 – 613 .

Carpenter SR et al . 2009 . Accelerate synthesis in ecology and environmental sciences . BioScience 59 : 699 – 701 .

Denver RJ Hopkins PM McCormick SD Propper CR Riddiford L Sower SA Wingfield JC . 2009 . Comparative endocrinology in the 21st century . Integrative and Comparative Biology 49 : 339 – 348 .

Donaldson ZR Young LJ . 2008 . Oxytocin, vasopressin, and the neurogenetics of sociality . Science 322 : 900 – 904 .

Epperson LE Dahl TA Martin SL . 2004 . Quantitative analysis of liver protein expression during hibernation in the golden-mantled ground squirrel . Molecular and Cellular Proteomics 3 : 920 – 933 .

Freudiger CW Min W Saar BG Lu S Holtom GR He C Tsai JC Kang JX Xie XS . 2008 . Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy . Science 322 : 1857 – 1861 .

Giardine B et al . 2005 . Galaxy: A platform for interactive large-scale genome analysis . Genome Research 15 : 1451 – 1455 .

Grant B . 2009 . Sample, don't trample . The Scientist 23 : 20 – 22 .

Green RE et al . 2006 . Analysis of one million base pairs of Neanderthal DNA . Nature 444 : 330 – 336 .

Haussler D et al . 2009 . Genome 10K: A proposal to obtain whole-genome sequence for 10,000 vertebrate species . Journal of Heredity 100 : 659 – 674 .

Houle D . 2009 . Numbering the hairs on our heads: The shared challenge and promise of phenomics . Proceedings of the National Academy of Sciences 107 ( suppl. 1 ): 1793 – 1799 .

Hyduke DR Palsson B∅ . 2010 . Towards genome-scale signalling-network reconstructions . Nature Reviews Genetics 11 : 297 – 307 .

Jenner RA Wills MA . 2007 . The choice of model organisms in evo-devo . Nature Reviews Genetics 8 : 311 – 319 .

Kuehn BM . 2008 . 1000 genomes project promises closer look at variation in human genome . Journal of the American Medical Association 300 : 2715 .

Ling X Jiang J He X Mei Q Zhai CX Schatz B . 2007 . Generating gene summaries from biomedical literature: A study of semi-structured summarization . Information Processing and Management 43 : 1777 – 1791 .

Makris NC Ratilal P Jagannathan S Gong Z Andrews M Bertsatos I Olav RG Nero RW Jeck JM . 2009 . Critical population density triggers rapid formation of vast oceanic fish shoals . Science 323 : 1734 – 1737 .

Mandoli DF Olmstead R . 2000 . The importance of emerging model systems in plant biology . Journal of Plant Growth Regulation 19 : 249 – 252 .

Millar CD Huynen L Subramanian S Mohandesan E Lambert DM . 2008 . New developments in ancient genomics . Trends in Ecology and Evolution 23 : 386 – 393 .

Muller HM Kenny EE Sternberg PW . 2004 . Textpresso: An ontology-based information retrieval and extraction system for biological literature . PLoS Biology 2 : e309 .

[NRC] National Research Council . 2009 . A New Biology for the 21st Century: Ensuring the United States Leads the Coming Biology Revolution . National Academies Press .

Pevzner P Shamir R . 2009 . Computing has changed biology: Biology education must catch up . Science 325 : 541 – 542 .

Satterlie RA Pearse JS Sebens KP . 2009 . The black box, the creature from the black lagoon, August Krogh, and the dominant animal . Integrative and Comparative Biology 49 : 89 – 92 .

Schwenk K Padilla DK Bakken GS Full RJ . 2009 . Grand challenges in organismal biology . Integrative and Comparative Biology 49 : 7 – 14 .

Shi L Perkins RG Fanh H Tong W . 2008 . Reproducible and reliable microarray results through quality control: Good laboratory proficiency and appropriate data analysis practices are essential . Current Opinions Biotechnology 19 : 10 – 18 .

Stein LD . 2008 . Wiki features and commenting: Towards a cyberinfrastructure for the biological sciences: Progress, visions and challenges . Nature Reviews Genetics 9 : 678 – 688 .

Tomanek L . 2010 . Environmental proteomics: Changes in the proteome of marine organisms in response to environmental stress, pollutants, infection, symbiosis and development. Annual Review of Marine Science. (29 September 2010 ; www.annualreviews.org/doi/pdf/10.1146/annurev-marine-120709-142729 )

Wake MH . 2008 . Integrative biology: Science for the 21st century . BioScience 58 : 349 – 353 .

Weigel D Mott R . 2009 . The 1001 genomes project for Arabidopsis thaliana . Genome Biology 10 : 107 – 111 .

Whitesides GM . 2006 . The origins and the future of microfluidics . Nature 442 : 368 – 373 .

Wikelski M Kays RW Kasdin J Thorup K Smith JA Cochran WW Swenson GW Jr . 2007 . Going wild: What a global small-animal tracking system could do for experimental biologists . Journal of Experimental Biology 210 : 181 – 186 .

Wu S Ying G Qiang W Capecchi MR . 2007 . Toward simpler and faster genome-wide mutagenesis in mice . Nature Genetics 39 : 922 – 930 .

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Integrating 21st century skills into education systems: From rhetoric to reality

Subscribe to the center for universal education bulletin, ramya vivekanandan rv ramya vivekanandan senior education specialist, learning assessment systems - gpe secretariat.

February 14, 2019

This is the third post in a series about  education systems alignment in teaching, learning, and assessing 21st century skills .

What does it mean to be a successful learner or graduate in today’s world? While in years past, a solid acquisition of the “three Rs” (reading, writing, and arithmetic) and mastery in the core academic subjects may have been the measure of attainment, the world of the 21 st century requires a radically different orientation. To participate effectively in the increasingly complex societies and globalized economy that characterize today’s world, students need to think critically, communicate effectively, collaborate with diverse peers, solve complex problems, adopt a global mindset, and engage with information and communications technologies, to name but just a few requirements. The new report from Brookings, “ Education system alignment for 21st century skills: Focus on assessment ,” illuminates this imperative in depth.

Recognizing that traditional education systems have generally not been preparing learners to face such challenges, the global education community has increasingly talked about and mobilized in favor of the changes required. This has resulted in a suite of initiatives and research around the broad area of “21st century skills,” which culminated most notably with the adoption of Sustainable Development Goal 4 and the Education 2030 agenda, including Target 4.7, which commits countries to ensure that learners acquire knowledge and skills in areas such as sustainable development, human rights, gender equality, global citizenship, and others.

In this landscape, Global Partnership for Education (GPE) has a core mandate of improving equity and learning by strengthening education systems. GPE supports developing countries, many of which are affected by fragility and conflict, to develop and implement robust education sector plans. Depending on the country, GPE implementation grants support a broad range of activities including teacher training, textbook provision, interventions to promote girls’ education, incentives for marginalized groups, the strengthening of data and learning assessment systems, early childhood education, and many other areas.

This work is buttressed by thematic work at the global level, including in the area of learning assessment. The strengthening of learning assessment systems is a strategic priority for GPE because of its relevance to both improving learning outcomes and ensuring effective and efficient education systems, which are two of the three key goals of the GPE strategic plan for the 2016-2020 period . The work on learning assessment includes the Assessment for Learning (A4L) initiative, which aims to strengthen learning assessment systems and to promote a holistic measurement of learning.

Under A4L, we are undertaking a landscape review on the measurement of 21st century skills, using a definition derived from Binkley et. al . and Scoular and Care :

“21st century skills are tools that can be universally applied to enhance ways of thinking, learning, working and living in the world. The skills include critical thinking/reasoning, creativity/creative thinking, problem solving, metacognition, collaboration, communication and global citizenship. 21st century skills also include literacies such as reading literacy, writing literacy, numeracy, information literacy, ICT [information and communications technologies] digital literacy, communication and can be described broadly as learning domains.”

Using this lens, the landscape review examines the research literature, the efforts of GPE partners that have been active in this space, and data collected from a sample of countries in sub-Saharan Africa and Asia in regard to the assessment of these skills. These research efforts were led by Brookings and coordinated by the UNESCO offices in Dakar and Bangkok. As another important piece of this work, we are also taking stock of the latest education sector plans and implementation grants of these same countries (nine in sub-Saharan Africa and six in Asia), to explore the extent to which the integration of 21st century skills is reflected in sector plans and, vitally, in their implementation.

Though the work is in progress, the initial findings provide food for thought. Reflecting the conclusions of the new report by Brookings, as well as its earlier breadth of work on skills mapping, a large majority of these 15 countries note ambitious objectives related to 21st century skills in their education sector plans, particularly in their vision or mission statements and/or statements of policy priorities. “Skills” such as creativity and innovation, critical thinking, problem-solving, decisionmaking, life and career skills, citizenship, personal and social responsibility, and information and communications technology literacy were strongly featured, as opposed to areas such as collaboration, communication, information literacy, and metacognition.

However, when we look at the planned interventions noted in these sector plans, there is not a strong indication that countries plan to operationalize their intentions to promote 21st century skills. Not surprisingly then, when we look at their implementation grants, which are one of the financing instruments through which education sector plans are implemented, only two of the 15 grants examined include activities aimed at promoting 21st century skills among their program components. Because the GPE model mandates that national governments determine the program components and allocation of resources for these within their grant, the bottom line seems to echo the findings of the Brookings report: vision and aspiration are rife, but action is scarce.

While the sample of countries studied in this exercise is small (and other countries’ education sector plans and grants may well include integration of 21st century skills), it’s the disconnect between the 15 countries’ policy orientation around these skills and their implementation that is telling. Why this gap? Why, if countries espouse the importance of 21st century skills in their sector plans, do they not concretely move to addressing them in their implementation? The reasons for this may be manifold, but the challenges highlighted by the Brookings report in terms of incorporating a 21 st century learning agenda in education systems are indeed telling. As a field, we still have much work to do to understand the nature of these skills, to develop learning progressions for them, and to design appropriate and authentic assessment of them. In other words, it may be that countries have difficulty in imagining how to move from rhetoric to reality.

However, in another perspective, there may be a challenge associated with how countries (and the broader education community) perceive 21st century skills in general. In contexts of limited resources, crowded curricula, inadequately trained teachers, fragility, weak governance, and other challenges that are characteristic of GPE partner countries, there is sometimes an unfortunate tendency to view 21st century skills and the “basics” as a tradeoff. In such settings, there can be a perception that 21st century skills are the concern of more advanced or higher-income countries. It is thus no wonder that, in the words of the Brookings report, “a global mobilization of efforts to respond to the 21CS [21st century skills] shift is non-existent, and individual countries struggle alone to plan the shift.”

This suggests that those who are committed to a holistic view of education have much work to do in terms of research, sharing of experience, capacity building, and advocacy around the potential and need for all countries, regardless of context, to move in this direction. The Brookings report makes a very valuable contribution in this regard. GPE’s landscape review, which will be published this spring, will inform how the partnership thinks about and approaches 21st century skills in its work and will thereby provide a complementary perspective.

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Nathan Phillips , William H Teale , Michael Manderino

In the New Literacies and Digital Epistemologies Series (M. Knobel & C. Lankshear, Eds.)

JuliAnna Ávila

English Update: A Newsletter from the Center on English Learning and Achievement

Sherra-Lee Robinson

Brock Dubbels

Hameed Olalekan Bolaji

International Journal of Technology and Design Education

Douglas Kellner

Evan Ortlieb

Hannah Gerber

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Science in the 21st Century

From the invention of new life forms to the discovery of life beyond Earth, science is reshaping our understanding of the universe in the twenty-first century. In the Summer 2012 issue of  Dædalus , leading scientists describe emerging advances in nanoscience, neuroscience, genetics, paleontology, microbiology, mathematics, planetary science, and plant biology, among other areas. The authors examine how their disciplines might address some of this century’s most critical challenges, such as treating an explosion of degenerative neurological disease and providing food, fuel and a habitable environment for a global population predicted to reach nine to ten billion by 2050.

The Search for Habitable Worlds: Planetary Exploration in the 21st Century

The search for and detailed characterization of habitable environments on other worlds – places where liquid water, heat/energy sources, and biologically important organic molecules exist or could have once existed – is a major twenty-first-century goal for space exploration by NASA and other space agencies, motivated by intense public interest and highly ranked science objectives identified in recent National Academy decadal surveys.

E pluribus unum : From Complexity, Universality

In a brief survey, Terence Tao discusses some examples of the fascinating phenomenon of universality in complex systems, in which universal macroscopic laws of nature emerge from a variety of different microscopic dynamics.

Small Machines

Over the last fifty years, small has emerged as the new big thing. The reduction of information and electronics to nanometer dimensions has revolutionized science, technology, and society. Now scientists and engineers are creating physical machines that operate at the nanoscale. Using approaches ranging from lithographic patterning to the co-opting of biological machinery, new devices are being built that can navigate, sense, and alter the nanoscale world. In the coming decades, these machines will have enormous impact in fields ranging from biotechnology to quantum physics, blurring the boundary between technology and life.

Can We Progress from Solipsistic Science to Frugal Innovation?

Energy demand in the twenty-first century will be driven by the needs of three billion people in the emerging world and three billion new inhabitants to our planet. To provide them with a renewable and sustainable energy supply is perhaps the greatest challenge for science in the twenty-first century. The science practiced to meet the energy needs of the twentieth century responded to a society of wealth, and energy systems were designed to be large and centralized.

The Future of Fundamental Physics

Fundamental physics began the twentieth century with the twin revolutions of relativity and quantum mechanics, and much of the second half of the century was devoted to the construction of a theoretical structure unifying these radical ideas. But this foundation has also led us to a number of paradoxes in our understanding of nature.

Microbes as Menaces, Mates & Marvels

The conventional understanding of microbes as causative agents of disease has led us to fear them and to consider them our deadly enemies. Much less appreciated are the central roles microbes play in shaping the environment and in maintaining plant, animal, and human health. All metazoan organisms – organisms that we can see with the naked eye – exist in lifelong partnerships with vast microbial communities.

Fossils Everywhere

History is omnipresent in the natural world, from inside rocks on the continents to the genes, cells, and organs of each creature on the planet. Linking the historical records of rocks, fossils, and genes has been a boon to understanding the major events in evolution. We use these seemingly different lines of evidence as tools for discovery.

Deciphering the Parts List for the Mechanical Plant

The development of inexpensive DNA sequencing technologies has revolutionized all aspects of biological research. The proliferation of plant genome sequences, in conjunction with the parallel development of robust tools for directed genetic manipulation, has given momentum and credibility to the goal of understanding several model plants as the sum of their parts.

The Coming Epidemic of Neurologic Disorders: What Science Is - and Should Be - Doing About It

The Earth’s population is aging fast, and the coming sharp increase in the number of people over age sixty-five will bring with it an epidemic of age-related neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases. Currently, no cures exist for the major neurologic disorders. Unless cures can be found, by 2050 the cost of these diseases will exceed $1 trillion annually in the United States.

Biodiversity & Environmental Sustainability amid Human Domination of Global Ecosystems

Concern about the loss of Earth’s biological diversity sparked two decades of research of unprecedented intensity, intellectual excitement, and societal relevance. This research shows that biodiversity is among the most important factors determining how ecosystems function. In particular, the loss of biodiversity decreases the productivity, stability, and efficiency of terrestrial, freshwater, and marine ecosystems.

Toward an understanding of 21st-century skills: From a systematic review

• October 2021
• International Journal for Educational and Vocational Guidance 23(2)

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