A Complex Adaptive System Perspective

Journal of Engineering Education July 2011, Vol. 100, No. 3, pp. 417–423 © 2011 ASEE. http://www.jee.org

Guest Editorial Accelerating STEM Capacity: A Complex Adaptive System Perspective RICK STEPHENS AND MICHAEL RICHEY The Boeing Company

THE PERCEIVED PROBLEM AND OPPORTUNITY The traditional U.S. approach to learning—one focused primarily on formal education—is inadequately preparing the future workforce for competitiveness-critical occupations. A new approach—one that encourages formal education and practical application supports lifelong learning and offers potential to turn the tide.

Few would argue against the need for a continuous flow of creative, intelligent people into the workforce over the next couple of decades to maintain the United States’ competitive edge in the global economy. Many see serious challenges to our ability to remain in front. “The strength and versatility of [our] labor force, [and] its capacity to nourish research and innovation are increasingly dependent on an education system capable of producing a steady supply of young people well-prepared in science and math,” (Education Commission of the States, 2011, p. 1). This report draws attention to the endemic and pervasive weaknesses in models that fail to support a flourishing workforce competency.

“All along the pipeline—from the quality of science instruction in the early grades, to the performance of high school seniors on international tests, to the content and rigor of teacher education programs in the nation’s colleges and universities—signs of weakness and deterioration exist” (Education Commission of the States, 2011, p. 1; Stephens, 2010). At the same time, analysts agree that over these same couple of decades, the fastestgrowing occupations are projected to be in areas of science, technology, engineering, and mathematics (STEM).

Providing the needed supply of qualified candidates to fill these positions is a national challenge (given the performance trends within the system) which is made more difficult by the aging and retirement of the current STEM-educated workforce. Many attempts have been made to open a dialogue at the intersection of the learning sciences and to understand challenges for maintaining a healthy pipeline from K-1 through to the workplace (Bransford et al., 2005). Yet declining trends in STEM graduates’ competencies persist despite reform efforts that have spanned the last 30 years (Axelrod, 2010; Camp, 1997; & Haney, 2004).

For many years, most of the ongoing research in the fields of Learning Sciences, Social Sciences, and Engineering Education Research has been directed primarily at understanding learning and teaching processes within formal educational environments such as schools. These research fields are changing and now include informal as well as formal learning opportunities (Bell, Shouse, Lewenstein, & Feder, 2009; Bransford et al. 2005; 2006). A more holistic, ecosystem view includes a new emphasis on workplace learning.

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The workplace, it turns out, is an excellent setting to allow formal and informal learning to be more explicitly connected (e.g., competencies and learning strategies taught in the formal environment are directly linked to student learning and performance within the workplace). If the educational ecosystem investment (formal and informal) is viewed as a portfolio, then funding investigations that look at education as a capacity-building system for the workplace should be considered. Such investigations would include analysis of the strengths and weaknesses of social settings that are essentially complex, adaptive social systems operating in a competitive global environment (Lemke & Sabelli, 2008; Maroulis et al., 2010).

What can the learning sciences bring to this world? And, in addition, what can the learning sciences and industry gain from a deep and structured investigation of this complicated learning environment? We argue here for a continued and committed research endeavor at this intersection of the learning sciences and the workplace; indeed the classroom and the workplace are where innovation and cross-fertilization can best occur for the mutual betterment of learners, communities, and businesses. At The Boeing Company, these issues are not abstract entities, but have a direct impact on day-to-day functioning implicit in our ability to compete globally. As industry partners, we are committed to support not only the intersection of the learning sciences and engineering education research, but also to the scientific process of exploration, discovery, confirmation, and dissemination.

Academia/industry partnerships can profoundly impact educational ecosystems by (i) integrating theories of learning with situative environments, (ii) linking rigorous research with real-world practice, and (iii) dissemination of successful methods and findings through peer-reviewed educational networks.

INDUSTRY—ACADEMIA BENEFITS OF ENGINEERING EDUCATION RESEARCH AND PRACTICE Boeing manages roughly 12,000 engineering and non-engineering courses within its portfolio, a total of 49,000 hours of instructional material. For instance, in 2009 Boeing instructors provided in excess of 7 million hours of instruction to more than 150,000 employees across 45 countries. By any measure, this is a tremendous instructional output, but it blends tightly with a vision of Boeing’s core competency, Large Scale Systems Integration. Empirical data has demonstrated that small but effective changes in teaching methodolgies at this scale produce large cumulative value in terms of increased efficiency and knowledge transfer throughout the enterprise. For these reasons, our research goals are focused on developing evidence-based methodologies that leverage shared resources, distributed expertise, intellect, and rigor (NRC, 2002).

Key strategies include: • Learning Laboratory: Building on the premise that people learn in both formal and informal environments, link across both settings to expand the conversation by including social impacts on learning to community, parents, educational structure, and policy.

• Innovation, Adoption, and Diffusion of Research: Researchers and practitioners collaborate to design and study systemic STEM and engineering research efforts, which intentionally link theories of both learning and practice. Promote adoption and awareness of engineering education research (EER) methods through the diffusion of innovative research within and beyond the workplace.

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Learning to Engineer: Partner and support EER efforts through enabling an extension of research outside traditional settings; conduct research in a “learning laboratory” sponsored by Boeing.

Engineering Assessments: Assist in understanding the aggregate components and implications of organizational learning curves, including the impact of knowledge transfer on product performance. Implement and measure formal assessments within a cyber-infrastructure.

ENGINEERING EDUCATION RESEARCH AT BOEING

The needs of the workplace differ greatly from those of a typical formal learning structure. The rapid pace associated with a culture of “innovate or die” coupled with continuous evolution of technology in industry presents relentless learning challenges to the practicing engineer. With the acceleration of changes in advanced materials, Product Lifecycle Engineering software tools and processes, engineers must “unlearn” skills and rapidly adopt new ones; a global, geographically dispersed, multicultural workforce compounds this challenge. These and other complex issues in the workplace learning environment make this a uniquely generative space. We believe that many innovative practices that have emerged as a result of the business community’s marketplace of practice could offer qualitative improvements to many classroom settings in the K-16 formal schooling system. The workplace is inherently a complex social environment; appropriate learning (or lack thereof) can generate immediate, authentic outcomes to individuals and programs. In this environment, innovative ideas like “just-in-time” coaching, “personalized learning environments,” peer assessments, performance-based measures, and semantic networking of distributive expertise can contribute to the field of the learning sciences. Many activities, which provide incentives and feedback for learners, could have similar impacts in formal classroom instruction. We look to engineering education research to help us find ways to understand, disseminate, and facilitate rapid change in engineering practice and beyond where appropriate.

Examples within The Boeing Company’s research portfolio include: Boeing (O’Mahony et al., 2010) demonstrated a complicated and networked social structure (which underlay the learning practices in course material) that increased the potential for more immediate impact on the individual’s skills and placement within the work team. In this University of Washington NSF LIFE Center study, research included the use of ethnographic and qualitative techniques to study instructor-led workplace learning.

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Engineers often made contributions to their own learning (e.g. by collaborating on learning problems) that augmented the information brought by the Subject Matter Experts (SMEs) and teachers.

Engineers became willing collaborators in their own learning by adding to the curriculum and initiating many consequential and deep-seated knowledge threads about subject matter. These Continuous Quality Improvement (CQI) feedback loops are essential components of successful modern companies.

Researchers who carried out this study found workplace learning to be an extremely useful source that helped them re-think many issues relating to teaching, learning, and assessments that typically are constrained in formal settings that are subject to

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high stakes testing. Indeed, many of the ideas that surfaced in the workplace research show potential for adaptation to K-16 formal classroom scenarios.

In addition, Boeing worked with another collaborative team from the University of Washington (Lawton et al., 2011) to explore how to structure online learning experiences so both formal and informal workplace learning can be integrated and made more effective. This involved measuring learning and the transfer of knowledge (both for individual learners and for the overall organization) as reflected in composite workplace measures such as productivity. A focus of this research was the integration of several types of assessments, such as formative, authentic, unobtrusive, and reflective. This generated a rich stream of data that provided feedback during learning and also supplied critical data for composite workplace measures. Key findings included: • Online courses can be developed collaboratively by a community of coaches, course developers, and engineers. Courses could be rapidly fielded, debugged and continuously improved.

• Learning Science Design principles and the use of a range of assessments can significantly improve online courses. Engineers who took redesigned courses were more engaged; they also exhibited better attitudes toward their future learning and less dependence on their level of initial subject knowledge.

• It is important to note the types of roles learners can have in online learning communities and tailor development and instruction to fit their natural patterns of use.

THE EDUCATION SYSTEM AS A COMPLEX ADAPTIVE SYSTEM The educational system that produces the critical talent for the United States’ future security and prosperity is complex (Axelrod, 2010). It is composed of systems nested within subsystems, each operating on multiple temporal scales where observable causality is often hidden (Miller & Page, 2007). Changes to this system emerge through evolutionary processes and are encumbered by complex physical, behavioral, and social phenomena as well as competing interests. Faced with overwhelming complexity in the learning ecosystem (including shifting economic, political, and business environments), we tend to focus primarily on issues that are relevant to the cultural boundaries within which we operate.” “[W]e are forced to fall back on rote procedures, habits, rules of thumb, and simple mental models to make decisions” (Sterman, 2000). The individual and collective adequacy of our disciplines shape and influence collective epistemic theories; these schema, in-turn, shape how people interpret meaning from the information they encounter, (Lemke & Sabelli, 2008).

Our Santa Fe Institute and SRI Inc. research is attempting to model educational subsystem behaviors through the lens of complex adaptive systems to better conceptualize the current educational ecosystem. Therefore, we plan on identifying methods to model the larger system. A deep understanding of this structure (exponential complexity encountered as knowledge is distributed through the organization) is required in order to transcend subcultural boundaries and meld a unified framework. From this might emerge a fresh composite that values different cultural and situational perspectives. Complex systems display many counterintuitive components and, upon reflection, an intricate interdependence make many things visible. Goals include: • Identifying the multiplicative effects of educational stakeholders who impact the equilibrium of the system, including perceived boundaries between policy, research, community, and practice

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Exploring the relationship of component-to-collective behavior (the relationship of internal structure to external influence) Identifying the dynamics of information and its computational characterization: adaptive networks, network modeling and analysis, visualization of networks, and agent-based modeling Exploring data-driven research methods such as analytical methods, nonlinear statistics, and methods and tools for complex systems education Exploring informal learning environments since formal education systems are only one factor influencing the capacity and capability of the learner.

A PARTNERSHIP FRAMEWORK FOR STEM EDUCATION

Within these constraints, Boeing has engaged in a series of interdisciplinary research projects to expand knowledge and better understand and improve education and learning.

Our aim is to move the U.S. toward continuous sustainable improvement in its educational system. As we think about the “ecosystem” and our moral responsibility to influence (through policy and outreach) an environment that enables humans to reach their full potential, we see gaps between the promise of research and the application to drive social change (Lemke & Sabelli, 2008). Our aim is to partner with stakeholders to uncover the educational structure and identify the institutional incentives that drive cultural and organizational change. While they might be useful for exploring certain aspects of the question, we believe that this problem cannot be solved by reductionist or top-down methods alone.

We think a viable solution will require a dynamic and collaborative approach. Understanding education from a complex adaptive systems approach requires interdisciplinary and trans-disciplinary collaborations that include government, concerned citizens, industry, communities, and academia. In this conversation, social scientists, technologists, instructors, policy makers, economists, cognitive scientists, and parents must work alongside government industry, and community experts to develop new models, a new culture with shared artifacts, practices, and behaviors, and a common framework that will help transform the U.S. educational system. The research initiative that Boeing has undertaken in this exploratory learning environment builds on other academia and industry partnerships, and it illuminates the pathway for others to enter and influence the field of scientific inquiry in engineering education research.

Through academia, industry, and government partnerships, we can successfully develop a foundation that will allow us to build a pipeline for knowledge workers—a criterion that is required to ensure our nation’s success. A cultural integration of this shape and size, requires all partners to honor the unique contribution that public and private members bring to the solution space. In the words of Thomas Kuhn, “…in both political and scientific development the sense of malfunction that can lead to crisis is prerequisite to revolution” (Kuhn, 1962). We believe that the time is now and the foundation for transformative change is at hand.

ACKNOWLEDGMENTS Special thanks to our colleagues from the LIFE Center at the University of Washington, Dr. John Bransford, Dr. Daryl Lawton and Dr. Kieran O’Mahony and Dr. Nora Sabelli from SRI International, the Center for Technology and Learning.

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