Facilitators: Matthijs Koopmans, Mercy College, USA & Hiroki Sayama, Binghamton University, USA
Wednesday, September 20, 2017
Objectives and Scope of Coverage: As complex systems science has matured as an interdisciplinary field, its paradigms and essential concepts, such as dynamical systems, sensitivity in initial conditions, chaos, stochasticity, interdependence, self-organization, phase transition, learning, evolution, networks, multiscale properties and emergence, have been widely adopted in a number of scientific disciplines, from biology to social sciences and from engineering to medicine. To this date, educational research has remained heavily reliant on conventional paradigms, which permits only a limited range of questions about education to be investigated. Meanwhile, work on complexity in education has been largely theoretical and exploratory, without having the level of conceptual and methodological specificity that is required to capture the dynamical processes hypothesized in the complex systems literature, nor does it speak to the specific gaps in our knowledge that result from the relative absence of dynamical perspectives in empirical educational research. Likewise, the key concepts of complex systems have remained mostly excluded from science as taught in the K-16 education setting.
Recent progress in complex systems science includes significant and path-breaking empirical work to study the dynamical underpinnings of the educational process, and substantial development of structured, accessible educational materials about complexity. The proposed satellite symposium aims to present some of these advances at the intersection of educational research and complex systems science, and thereby promote cross-fertilization of ideas among researchers and growth of this important area of research and practice.
Session Schedule
PART I: COMPLEXITY TEACHING AND LEARNING
9:00 – 9:30
Education in the Information Age
Carlos Gersherson, Universidad Nacional Autónoma de México
9:30 – 10:00
NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts
Lori Sheetz, U.S. Military Academy at West Point, USA
Ralucca Gera, Naval Postgraduate School, USA
Jon Roginski, U.S. Military Academy at West Point, USA
Catherine Cramer, New York Hall of Science, USA
Stephen Uzzo, New York Hall of Science, USA
Emma Towlson, Northeastern University, USA &
Hiroki Sayama, Binghamton University, USA
10:00 – 10:30
The Math of Patterns: Teaching Higher-Level Mathematics Using Mixed Media Online
Yayoi Teramoto Kimura & Philippe H Trinh, Oxford University, UK
yayoi.teramoto@balliol.ox.ac.uk,
10:30 – 11:00 Coffee Break
11:00 – 11:30
An interview-based study of pioneering experiences in teaching and learning Complex Systems in Higher Education
Joseph Lizier, The University of Sydney, Australia
Michael Harre, The University of Sydney, Australia
Melanie Mitchell, Portland State University & Santa Fe Institute, USA
Simon De Deo, Carnegie Mellon University & Santa Fe Institute, USA
Conor Finn, The University of Sydney, Australia
Kristian Lindgren, Chalmers University of Technology, Sweden
Amanda Lizier, University of Technology Sydney, Australia
Hiroki Sayama, Binghamton University, USA
joseph.lizier@sydney.edu.au
11:30 – 12:00
Distilling the Santa Fe Institute Experience: The Complexity Challenge
Gabby Beans, Santa Fe Institute, USA
PART II: STUDYING COMPLEX PROCESSES IN EDUCATION
12:00 – 12:30
Exploring the Effects of Creating Small High Schools on Daily Attendance: A Statistical Case Study
Matthijs Koopmans, Mercy College, USA
12:30 – 13:00
Considering Time in Complex Systems Education Research
Gwen C. Marchand, University of Las Vegas, USA
Jonathan C. Hilpert, Georgia Southern University, USA
13:00 – 14:30 Lunch
14:30 – 15:00
Interaction Dominant Models and Theory Testing in Complex Systems Educational Research
Jonathan C. Hilpert, Georgia Southern University, USA
Gwen C. Marchand, University of Nevada, USA
15:00 – 15:30
Synthetic Understanding via Movement Analogies (SUMA Project): Physical Activities as a Basis of Trans-disciplinarity
Robert Hristovski, Ss. Cyril and Methodius University, Rep. of Macedonia
Natàlia Balagué , Pablo Vázquez, & Angels Massip, University of Barcelona, Spain
15:30 – 16:00
Brain strategies for mental skill development: Evidence and implications
Martin Gardiner, Brown University, USA
16:00 – 16:30 Coffee Break
16:30 – 18:30 Roundtable Discussions
19:00 Gala Dinner (Conference Wide) - Get your tickets at the conference website.
Session Abstracts
Education in the Information Age
Carlos Gersherson, Universidad Nacional Autónoma de México
Our species has had three major technological revolutions: agricultural, industrial, and informational. We can say that each of them has harnessed matter, energy, and information, respectively. Also, each revolution has transformed societies in general and education in particular. I will review the changes that our technologies have produced in education and focus on how computers have enabled us to study complex systems. Our recent understanding of complexity forces us to change how we teach. These changes will have an important impact on our species.
NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts
Lori Sheetz, U.S. Military Academy at West Point, USA, Ralucca Gera, Naval Postgraduate School, USA, Jon Roginski, U.S. Military Academy at West Point, USA, Catherine Cramer, New York Hall of Science, USA, Stephen Uzzo, New York Hall of Science, USA, Emma Towlson, Northeastern University, USA, & Hiroki Sayama, Binghamton University, USA
Generations have long been described by the science of their time. The last 200 years provides a set of familiar examples: the landscape of the Gilded Age was dominated by labor, vast in its quantity and skill. The Second Industrial Revolution was characterized by machines and the science of mass production. In the Information Age, science and technology enabled exponential increases in speed and miniaturization. However, those countries, businesses, and people who remain fixated on the science of the day are left behind tomorrow. These shifts to the next generation of scientific advancement continue. Today the world needs new science to understand the implications of large scale connectedness - less at the individual level, and more at that of the entire system. Network Science provides a framework with which to study our world as it is becoming defined by connectivity. As the world continues to change, one may expect education to likewise evolve and adapt. However, in many ways (though not all) our educational system functions in the same way as it did in the early 20th century when we broke apart the "one-room schoolhouse" and began teaching different age groups at different times in different rooms. Linear thinking persists in our educational system now just as it did then. Although we now live in a highly non-linear environment that can be characterized by networks and connectivity, network elements do not enter the educational lexicon until the Master’s and PhD levels of education. Yet younger students already have a natural intuition about networks because they live connected lives. This natural inclination could and should be leveraged as we introduce alternative methods for teaching the challenging subjects that include network elements, making these subjects more attainable to more students. Many educators have recognized that it is past time for our educational system to be updated to reflect the interconnected world in which we live.
We organized the NiCE (Networks in Classroom Education) Teacher Workshop (http://bit.ly/2s9GlRH) to facilitate the conversations and collaborations between educators and scientists necessary to address this demand for a revolutionary update to our educational system. It was held at the United States Military Academy at West Point as a four-day workshop on July 10-13, 2017, with around 30 participants that encompassed teachers and administrators (whose expertise spanned disciplines from math to remedial reading, across the whole K-12 range), and a number of experts in Network Science. Its goal was to educate K-12 teachers and administrators about Network Science, and to enable and empower those teachers and administrators to bring network thinking and ideas to their students, schools, and districts. During the workshop, network thinking was not only presented as concepts to be taught to students, but also actively utilized as a tool to make curriculum development and delivery easier and more successful, and to explore and explicate school-wide challenges. The participating teachers and administrators developed presentations and concrete lesson plans that utilized Network Science and network thinking. These ideas and plans collectively demonstrate tremendous opportunities to improve education, both by quantitatively identifying curricular elements central to interdisciplinary learning and describing them with this lens, and by systematically examining the curriculum and standards, and exploiting network thinking to meet their requirements. Ultimately, these central topics may be accessible to a greater range of students, via methods that are preferential to and easier for the educators delivering them.
The Math of Patterns: Teaching Higher-Level Mathematics Using Mixed Media Online
Yayoi Teramoto Kimura, Oxford University, UK
yayoi.teramoto@balliol.ox.ac.uk,
Philippe H Trinh, Oxford University, UK
The Mathematics of Patterns is an award-winning website containing educational resources for the study of pattern formation in nature, through Turing or diffusion-limited instabilities. While undergraduate-level mathematics courses in dynamical systems focus heavily on technical aspects of solving differential equations, many of the concepts in these courses lend themselves to being understood visually in a more intuitive fashion. As such, the aim of the project was to produce easily-digestible audio and visual media for learning theoretical and numerical concepts in mathematical modelling, reaction-diffusion equations, and dynamical systems.
I will present a summary of the project and how it has been received by the community of applied mathematicians, lecturers, and students worldwide in the four years from when the notes were first posted. Although the original target audience were undergraduate mathematics students, surprisingly, the website has reached a wider non-specialist audience through popular science communication channels that have used the online materials (e.g. Minute Earth and Blablalogia). I will discuss what I learned from this experience, and the workflow and commitment involved in creating such materials. Finally, I hope to discuss the challenges that lie ahead for academics who are interested in creating such audio-visual media as part of their teaching or public engagement.
Website: http://www.theshapeofmath.com/princeton/dynsys
Report: http://www.pacm.princeton.edu/documents/Kimura.pdf
An Interview-Based Study of Pioneering Experiences in Teaching and Learning Complex Systems in Higher Education
J. T. Lizier, M. Harre, & C. Finn, The University of Sydney, Australia
M. Mitchell, & S De Deo, Santa Fe Institute, USA
K. Lindgren, Chalmers University of Technology, Sweden
A. L. Lizier, University of Technology Sydney, Australia
Due to the interdisciplinary nature and reach of complex systems as a field, students undertaking courses in complex systems at University level have diverse backgrounds across physics, mathematics, computer science, engineering, biology, neuroscience, economics, social sciences and the humanities. This brings challenges (e.g. diversity of skills, computer programming and analysis ability) but also opportunities (e.g. facilitating interdisciplinary interactions and projects, and applications that meet disciplinary needs) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities in more depth, by examining the primary body of knowledge currently residing in the experience of pioneering teachers and learners in this space. We report an interview-based study of several such subjects (conducted amongst the authors) on their experiences, and a discussion and analysis comparing and contrasting those experiences. Our discussions explore: how curriculum design was approached, how theories/models/frameworks of teaching and learning informed their decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We find a striking level of commonality in the issues expressed as well as the strategies to handle them. For example: there was a significant focus on problem- or activity-based learning; a focus on understanding and applying key principles with technical analysis and programming implementation as a means to this end; and the use of major student-led creative projects for both achieving and assessing learning outcomes. While similar approaches to curriculum design (e.g. constructive alignment) were observed, curriculum content was the main area recognised as being contested since the field is still rapidly evolving, however this can be interpreted as a strength of the field in tightly knitting research and teaching into the one community.
Distilling the Santa Fe Institute Experience: The Complexity Challenge
Gabby Beans, Santa Fe Institute, USA
The Santa Fe Institute is using online learning to promote the understanding and application of key concepts in complexity science. Along with the "traditional" use of online teaching methods such as MOOCS, the Institute has created a novel, capstone learning experience for our online students that encourages them to apply the key concepts they learned from the various MOOCS to generate a solution to an open-ended project tied to a real-world application. This Complexity Challenge, piloted in August and September of 2017, provides a nice method by which students can actively apply and synthesize their online learning in a context that is inherently self motivating. Submitted solutions are initially peer reviewed and the better ones are then ranked by faculty mentors and outside experts, with prizes awarded to the top tier. Here we outline the key elements of this Challenge-based methodology, and summarize lessons learned from the pilot project.
Exploring the Effects of Creating Small High Schools on Daily Attendance: A Statistical Case Study
Matthijs Koopmans, Mercy College, USA
This study is concerned with the question whether the creation of small high schools has a favorable impact on daily attendance rates in those schools. The determinants of daily school attendance are under-researched in education, particularly considering the ongoing concerns about students leaving school prematurely. Reducing the likelihood of students dropping out through individualized attention and support is part of the rationale for the small high schools movement, and therefore, the impact of small high schools creation on student attendance is relevant. Are attendance rates higher in a given school after than before the initiation of such initiatives and do they show greater stability and predictability afterward? The former question can be addressed through conventional summary statistics (mean, range), the latter question requires a detailed description of attendance rates sequentially ordered over a longer time period. The methodology for analyzing such information is available (e.g., Beran, 1994), but rarely used in education.
In this study, the daily attendance rates in one public school in New York City (School A) are analyzed over a seven-year period, i.e., from September 2007 through June 2014 (N = 1,245). School A enrolled approximately 900 students up to and through the 2009-2010 school year. Afterward, enrollment was reduced to about 250 students and remained stable for the period under study. Two complementary analytical approaches were used to estimate the effects of the enrollment reduction in this case. First, the impact of perturbation on the series was estimated, particularly in connection with the point of transition to a small school, using intervention analysis (Peña, 2001). Second, the statistical properties of the temporal process before and after the transition were examined for signs of meta-stability and self-organized criticality (Bak, 1996), using a fractional differencing approach (Beran, 1994).
Higher average attendance, as well as a gradual increase in the rates, was found in the period after the transition, although the immediate upward jump at the beginning of the 2010-11 school year is followed by a relapse. However, the findings indicate that overall, the underlying dynamics of attendance rates were favorably impacted by the small high schools implementation, i.e., they were more stable after than before the transition to the small high school format. Specifically, the analyses reveal meta-stability (edge of chaos) after the change, and instability (Brownian motion) before. These processes would have remained hidden if conventional statistics were used, making the case for a thorough analysis of temporal patterns in educational research, and the engagement of educational researchers in the use of the requisite methodology. Implications and limitations of the study are discussed.
References
Bak, P. (1996). How nature works: The science of self-organized criticality. New York: Springer.
Beran, J. (1994). Statistics for long-memory processes. Boca Raton, FL: Chapman & Hall/CRC.
Peña, D. (2001). Outliers, influential observations, and missing data. In D. Peña, G. C. Tiao, & R. S. Tsay (Eds.) A course in time series analysis (pp. 136-170). New York: Wiley & Sons, Inc.
Considering Time in Complex Systems Education Research
Gwen C. Marchand, University of Las Vegas, USA
Jonathan C. Hilpert, Georgia Southern University, USA
Valisner (2008) defines education as the process of setting up conditions for a developing person to be open to innovation or change. From this perspective, educators should be invested in understanding how to meaningfully direct and support the transformation of the developing person into that which the person is becoming, rather than invested in investigation of the state of a person at any given point in time. Educational researchers have begun to explicitly use complex systems as a research paradigm to more adequately design and conduct research capable of capturing dynamic processes inherent in education (Jacobson, et al., 2016; Kaplan & Garner, in press; Koopmans & Stamovlasis, 2016). However, designing research in education from a complex systems perspective places a new set of demands on scholars, which includes engaging in a much deeper consideration of the nature of change of the phenomena of interest and how to treat time. Choices that researchers make about when to begin a study to determine the initial condition of the system or spacing of measurement points creates assumptions about causality, feedback, and patterns of change of our educational process of interest. This conceptual paper draws upon empirical examples to help researchers more explicitly consider how to address time in complex systems research in education.
Drawing upon the educational construct of engagement, we provide examples of the types of questions education researchers may consider asking of time and how to explicitly bind time and address issues of time from a design, measurement, and analytic standpoint. Theory suggests that student engagement is a dynamic process that exists both within and between people (Skinner et al., 2016). Ongoing reciprocal exchanges between person and context over time create self-reinforcing adaptive or maladaptive patterns of engagement, which in turn influence learning. These types of effects are described as “self amplifying” (Skinner & Pitzer, 2012, p. 34) and become autocatalytic (Kauffman, 1989). When engagement becomes autocatalytic it self-regulates and self-replicates with less energy and conscious deliberation. Producing empirical evidence of these states is a challenge for the field. We might ask the following questions: How much time needs to be observed to understand the dynamics of engagement at different levels of analysis? How many observations within a given time period are required to understand the self-amplifying states that provide enhanced learning? At what point in a dynamic period does adaptive engagement translate to learning benefits or create a buffer against academic challenges or setbacks?
The complex systems education researcher cannot define their system of interest with a cursory understanding of time, bounding the change process in convenience (e.g., the beginning and end of a semester) even if eventually practical considerations dictate a balance between theoretically or empirically indicated change processes and convenience during the design of the study itself. Taking time seriously includes forefronting time as a critical element in creating boundaries around the system and including time as a design element in research.
References
Jacobson, M. J., Kapur, M., & Reimann, P. (2016). Conceptualizing Debates in Learning and Educational Research: Toward a Complex Systems Conceptual Framework of Learning. Educational Psychologist, 1-9. http://dx.doi.org/10.1080/00461520.2016.1166963
Kaplan, A. & Garner, J. (in press). A Complex Dynamic Systems Perspective on Identity and Its Development: The Dynamic Systems Model of Role Identity. Developmental Psychology.
Kauffman, S. A. (1989). Principles of adaptation in complex systems. Lectures in the Sciences of Complexity, 1, 619-712.
Koopmans, M., & Stamovlasis, D. (Eds.). (2016). Complex Dynamical Systems in Education: Concepts, Methods and Applications. Springer. http://dx.doi.org/10.1007/978-3-319-27577-2
Skinner, E. A., & Pitzer, J. (2012). Developmental dynamics of engagement, coping, and
everyday resilience. In S. Christenson, A. Reschly, & C. Wylie (Eds.), The Handbook of Research on Student Engagement (pp. 21-45). New York: Springer Science.
Skinner, E. A. (2016). Engagement and disaffection as central to processes of motivational
resilience and development. Handbook of Motivation at School. 2nd ed. New York, NY: Routledge, 145-68.
Valsiner, J. (2008). Open intransitivity cycles in development and education: Pathways to
synthesis. European journal of psychology of education, 23(2), 131-147.
Interaction Dominant Models and Theory Testing in Complex Systems Educational Research
Jonathan C. Hilpert, Georgia Southern University, USA
Gwen C. Marchand, University of Las Vegas, USA
The purpose of this presentation is to describe an approach to research design that can be used to guide complex systems research in educational contexts. Theoretical postulations in education often describe interaction dominant phenomena with complex, dynamic, and emergent properties. However, within a linear deterministic paradigm, these properties are often left out of theoretical models, which utilize mechanical assumptions that are conducive to statistical tests. In this presentation we will define the ontological differences between interaction dominant models (i.e. those used to model complex systems) and component dominant models (i.e. those used to model mechanical systems). We then discuss some analytical possibilities for drawing conclusions about interaction dominant systems. We end with some existing examples that help to illustrate the techniques.
A complex system is a collection of interacting components that gives rise to complex behavior (Mitchell, 2009). In education contexts system components can take material, conceptual, or semiotic forms such as individual students teachers and technological objects, motivation, behavioral, affective, epistemological, and cognitive variables, or words, text, symbols, and discourses (Bunge, 2004). Components within complex systems interact over time to produce outcomes at higher levels of analysis that are more than the sum of their parts, meaning the complex behavior cannot be reduced to the components that make up the system (Holland, 2006).
Interaction dominant systems (i.e. those that are complex) are based on the philosophical notion that outcomes emerge from the coordination of system components across temporal scales (Kaufmann, 1993). Although the system cannot be reduced, critical indicators can be identified that give insight into the overall functioning of the system. The relationship among the system components is considered nonlinear, meaning that the strength and direction of the relationships change over time and are dependent upon other system components. Supervenience describes system maintenance, where emergent states influence the equilibrium of interactions among system components. Because all system components are dependent upon one another, the dynamic behavior of critical indicators over time contains information about the entire system (Takens, 1981).
In contrast, a component dominant system can be precisely defined by its components, so that outcomes can be nearly perfectly reduced (Holden, Van Orden, & Turvey, 2009). Component dominant systems are based on the philosophical notion that a system can be reduced to components that adequately describe it. Further, the relationship among the components is considered linear, meaning that the strength and direction of the relationships are stable across time, or nomothetic (Hempel, 1965). Feedback loops describe system maintenance, where independent variables influence dependent variables in a cyclical fashion. Because all system components are considered independent of each other, the behavior of the systems is defined by a linear, deterministic causal mechanism (Chronbach & Meehl, 1955).
Theory building and testing are accomplished through the development of two forms of related submodels: a) the conceptual or theoretical model and b) the statistical model. These two models are considered a simplification or approximation of more complex conceptual or social systems (Sloane, 2006). This presentation will lay out the ontological assumptions of interaction dominant model building for complex systems research in a way that is useful for education researchers.
References
Bunge, M. (2004). Clarifying some misunderstandings about social systems and their mechanisms. Philosophy of the social sciences, 34(3), 371-381.
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological bulletin, 52(4), 281.
Hempel, C. G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York.
Holden, J. G., Van Orden, G. C., & Turvey, M. T. (2009). Dispersion of response times reveals cognitive dynamics. Psychological Review, 116(2), 318.
Holland, J. H. (2000). Emergence: From chaos to order. OUP Oxford.
Kauffman, S. A. (1993). The origins of order: Self organization and selection in evolution. Oxford University Press, USA.
Mitchell, M. (2009). Complexity: A guided tour. Oxford University Press.
Sloane, F. (2006). Normal and design sciences in education: Why both are necessary. Educational design research, 19-44.
Takens, F. (1981). Detecting strange attractors in turbulence. In D. A. Rand and L.-S. Young. Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898. Springer-Verlag. p. 366–381.
Synthetic Understanding via Movement Analogies (SUMA Project): Physical Activities as a Basis of Trans-disciplinearity
Robert Hristovski, Ss. Cyril and Methodius University, Rep. of Macedonia
Natàlia Balagué , Pablo Vázquez, & Angels Massip, University of Barcelona, Spain
In his UN manifesto ‘Seven complex lessons in education for the future’, Edgar Morin made a plea for an integrated approach in education. In his view, contemporary education, based on a fragmented structure of knowledge, limits reasoning and critical thinking in students and consequently doesn’t contribute enough to the development of an integrative knowledge (and competencies) - essential in modern society. The main issue, then, becomes how to integrate and reduce the barriers between widely different areas as STEM and Humanities. This issue will not be achieved by multidisciplinary and interdisciplinary approaches. We propose that this integration would be possible through teaching common concepts and principles of dynamical systems. Moreover, we claim that physical activities in a form of movement analogies (MA) may form the content of such an integrative education through formation of an embodied and experientially grounded understanding. The application of MA for teaching pluricontextual and transdisciplinary concepts is the objective of the proposed teaching methodology, that aims: 1) to help teachers and students to discover and learn the connecting dynamic conceptual patterns common to STEM and Hummanities, 2) to promote a synthetic understanding , and 3) to contribute to building a synthetic world view. The proposed embodied and experientially grounded-based understanding can be applied to all education levels, including early ages. It is expected that the development of learning transfer and integrative competencies in students will empower them to face the novel and challenging emergent problems of our society.
Brain strategies for mental skill development: Evidence and implications
Martin Gardiner, Brown University, USA
Our research concerning impact of musical learning on broader educational, cognitive, and social progress supports our hypothesis that the brain has variety of strategies for developing mental skills. Some are initially easier to develop but limit the extent of skill development in a particular area of application until they are augmented or replaced. Implications for education will be discussed. For physical and mental skills of all kinds to advance, the brain must form complex dynamical engagement systems to address dynamically demands that cannot fully be anticipated in advance. Our research provides some evidence concerning the strategic formation of such systems.
The first CCS Satellite Symposium on Complex Systems in Education: