Agenda, Abstracts and Speaker Bios

International Conference on Complex Systems 2018

Complex Systems: Literacy and Learning

July 26, 2018

AGENDA

6:00 Welcome and Introductions

6:10 Davar Ardalan:

Culture Meets Artificial Intelligence and Storytelling

6:25 Steve Uzzo:

Complexity and Lifelong Learning: The coupling of human and natural systems in a science center context

6:40 Emma Towlson:

NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts

6:55 Catherine Cramer:

Data Science for All: Situating Data Literacy Across Learning Settings

7:10 Break

7:30 Jamey Heit:

Failing Students and Teachers: Examining Education as a Complex System

7:45 Roxanne Moore:

System Dynamics to Improve Success & Sustainability of K-12 Educational Interventions

8:00 Patrick McQuillan:

Urban School Leadership & Adaptive Change: The “Rabbit Hole” of Continuous Emergence

8:15 Roundtable Discussion: The Role of Complexity in Lifelong Learning

8:45 End

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ABSTRACTS

Davar Ardalan: Culture Meets Artificial Intelligence and Storytelling

IVOW collaborates with scholars, educators, journalists, computer scientists, and AI experts to develop a new structured database to tag culture, tradition, and history. AI today represents a fairly narrow range of viewpoints, excluding the perspectives of tens of millions of people. This is because we have been looking at AI from the perspective of current data and algorithms. We now have the ability to look at AI from a interdisciplinary background--bringing together data science, enterprise and storytelling. We can leverage the philosophy of AI around patterns, and see what’s missing in large datasets and find new ways to apply it to areas such as customer and user engagement. Building authentic relationships with audiences, users and consumers requires inviting them to share who they are and what matters to them most--their traditions and cultural heritage. Making AI more inclusive requires us to invite the world to share their stories.

Davar Ardalan is the Founder and Storyteller in Chief of IVOW, a cultural storytelling startup powered by AI. Davar has been a journalist in public media for 25 years, most of those at NPR News, where she designed stories anchored in multiculturalism and steeped in historical context. In 2015, her last position at NPR was senior producer of the Identity and Culture Unit. Realizing that there is a gaping hole in AI algorithms that will define our future stories, Davar created IVOW, to crowdsource global narratives via a structured database. Ardalan co-chairs the Stories and Audiences Committee of the VR/AR Association, and has been recognized with a 2017 NASA Team Leadership award for Space Apps, a Gracie Award from the American Women in Radio and Television and a shout-out in the popular comic strip Zippy. In May 2014, she was the recipient of a United States Ellis Island Medal of Honor, for individual achievement and for promoting cultural unity.

Stephen Miles Uzzo: Complexity and Lifelong Learning: The coupling of human and natural systems in a science center context

Among the significant challenges for lifelong learning is the understanding of sustainability. Such topics as dynamic systems and the coupling of complex social and environmental systems that are needed to understand sustainability are largely unknown in formal school settings. It is therefore up to informal learning institutions, such as museums, nature centers, documentary-makers and other kinds of experiential learning institutions and learning resources to help deepen engagement of the populace with these difficult ideas. This talk will provide an overview of the development of an immersive museum exhibition on sustainability and the coupling of human and natural systems that acquaints groups of visitors of all ages with dynamic resource sharing, equilibria, feedback loops, tipping points and emergence. It will cover development and decision-making of the experience, how it is being used as a research platform, and how sophisticated analytics are helping to understand how visitors interact with the experience and each other during live sessions in the exhibition space.

Stephen Miles Uzzo is Chief Scientist for the New York Hall of Science, where he does research and development of public programs and experiences on complex science and systems dynamics. He also does instructional development for pre-service and in-service teacher education. Dr. Uzzo is also Adjunct Professor of Education for the New York Institute of Technology Graduate School of Education and Interdisciplinary Studies, where he teaches STEM integration of science, technology, engineering and mathematics into science instruction, instructional technology and integration of art into interdisciplinary teaching. His background includes teaching and learning in data driven science, computer graphics systems engineering, environmental science, and art history. He holds a terminal degree in Network Theory and Environmental Studies from the Union Institute School of Interdisciplinary Studies.

Emma Towlson: NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts

Our educational systems must prepare students for an increasingly interconnected future, and teachers require equipping with modern tools, such as network science, to achieve this. We held a Networks in Classroom Education (NiCE) workshop for a group of 21 K-12 teachers with various disciplinary backgrounds. The explicit aim of this was to introduce them to concepts in network science, show them how these concepts can be utilized in the classroom, and empower them to develop resources, in the form of lesson plans, for themselves and the wider community. Here we detail the nature of the workshop and present its outcomes - including an innovative set of publicly available lesson plans. We discuss the future for successful integration of network science in K-12 education, and the importance of inspiring and enabling our teachers.

Emma Towlson is a researcher in the Northeastern University Center for Complex Network Research (CCNR), with interests in the emerging field of Network Neuroscience. She has a Masters in Mathematics and Physics from the University of Warwick (2011), and received her PhD from the University of Cambridge (2015). Emma's expertise lies in investigating the topology and organisational principles of various kinds of brain networks, from C. elegans to mouse to human. She is currently working on applying and adapting techniques from network control theory to probe neuronal or near-neuronal level wiring diagrams from smaller organisms. Emma co-instructs Phys 5116: Complex Networks alongside Prof. Albert-László Barabási, and is invested in bringing Network Science approaches to broader audiences and educational settings.

Catherine B. Cramer: Data Science for All: Situating Data Literacy Across Learning Settings

Increasingly, the most important discoveries in contemporary science come from large-scale, data-driven approaches. This trend indicates that skills and knowledge in these approaches must play a significant role in 21st century STEM learning across all settings. It also means addressing issues of equity. Prosperity, innovation and security of individuals and communities increasingly depend on a data literate society. There must be a concerted effort to determine what it means to be a data literate citizen, information worker, researcher, or policymaker; to identify the quality of learning resources and programs intended to improve data literacy; and to chart a path forward that will bridge data science practice with learning, education and career readiness. The recent trend in infusing computational science into domains of STEM brings with it the need for a kind of data science literacy that is currently lagging in the computation in STEM revolution. Along with the need to use data in problem ideation and solutioning through facility with data science is the need to deal with the growing ethical and security issues emerging from the gathering and use of personal data for large scale commercial gain. For lifelong learners to become increasingly data savvy, deal with issues of data ownership, ethical use and security and be discriminating consumers and creators of data requires a kind of data literacy for everyone, or Data Science for All.

The challenges to cultivating data literacy are deeply aligned with multiple persistent challenges in STEM education more generally and get at some of the core issues of science learning, such as the nature of evidence and inquiry, and understanding the complexity of nature and ourselves. This talk will describe a capacity-building and collaborative inquiry process with learning and domain experts across diverse STEM domains to study and articulate the needs of lifelong learners in data science literacy; identify the unique and genuinely new dimensions of learning afforded by data-driven STEM; and how these dimensions create new opportunities to spark learning and insight in STEM domains that have been resistant to improvement for decades.

Catherine Cramer and has spent over twenty years engaged in public science education and identifying, creating, sustaining and growing productive and innovative collaborations and partnerships among research, industry and academia, with a particular focus on developing tools for the teaching and learning of complex network and data science. Her work sits at the intersection of network literacy, big data literacy, smart cities, and digital equity. All of her work has a goal of successfully developing collaborations with underserved communities. She is currently the manager of industry engagement at the Data Science Institute of Columbia University and is one of the lead developers of the data literacy and Social Network Analysis projects for the Northeast Big Data Innovation Hub.

Jamey Heit and Robin Donaldson: Failing Students and Teachers: Examining Education as a Complex System

Education is a network of some size, yet it has been relatively unimagined through the lens of complex systems theory. Given the structured patterns of behavior, this seems like a missed opportunity. This paper will examine a specific element of our education system: how students learn to write. After analyzing the inefficiencies in how writing is taught through tenets of Complex Systems Theory, this talk will articulate why similar tenets point to artificial intelligence (AI) technologies as a way to overcome the shortcomings that define the current educational system. The goal for this paper is to reimagine education as a dynamic system fueled by positive feedback loops based on redefining the strategies we encourage teachers to use based on new metrics of success.

The specific analysis of the problem and the possible strategies that technology can provide will be framed by examining education through four key questions as posited by Axelrod and Cohen in 2001: are problems long-term or widespread? Can the problem be evaluated through fast feedback loops? Is the problem a low risk for catastrophe from exploring new strategies? Is the problem a looming disaster? The answers to these four questions regarding education will structure the examination of education’s shortcomings as a system and identify specific ways in which AI software can lead to immediate and lasting improvements.

Jamey Heit currently serves as CEO of ecree, which provides the leading automated assessment technology on the market. He holds a PhD from Glasgow University and two Master’s degrees from Princeton. Prior to founding ecree, he taught across university disciplines for more than a decade. His award-winning research on technology’s impact on and in education has been presented around the world. He maintains his classroom roots by teaching Philosophy and the Humanities online at Walden University.

Roxanne Moore and Michael Helms: System Dynamics to Improve Success & Sustainability of K-12 Educational Interventions

K-12 schools and school systems are highly complex, exhibiting many interconnected relationships and feedback loops, making it difficult to predict the outcomes of potential policy changes. While system dynamics and agent-based modeling have rarely been applied to educational settings, new representations and system descriptions may enable more effective policies to be enacted. Currently, schools looking to change their performance, trajectory, or implement new curricula apply some type of intervention, or the same is done by an external partner. School settings are often crudely described using statistics about socioeconomic status, demographics, and test performance, while interventions are described in terms of their intended student outcomes. However, rarely is the compatibility of a particular school and a particular intervention taken into account, nor is the pathway or mechanisms for change clearly described.

In the context of such educational interventions, models could be used to depict the intricate relationships between students, teachers, administration, local and federal policies, the community in which the school is situated, and the intervening agency. Successful interventions require management strategies on multiple ‘levels’: the student level, the teacher level, the administrator level, and the community level. Our research shows that representing the school settings using visual diagrams that depict feedback loops present among the actors and attributes within K-12 school settings can facilitate discussion across various stakeholders and improve the chances of a successful, sustainable intervention.

In this work, causal loop diagrams of interactions between school settings and interventions are used to inform intervention development and iteration and to improve sustainability outcomes. The models were developed and refined over a 4-year grant period where the intervention is a novel computer science module for teaching programming using music remixing. These models highlight attributes that both help and hinder success in achieving the desired student-level outcomes, including improved content knowledge in programming and greater interest and engagement in computing. The models and their development facilitated discussion and decision-making among the intervention team, as illustrated by a qualitative analysis of interview data. Curriculum and teacher training changes were made as a direct result of models created from classroom observation data. Finally, we will discuss the ‘predictive’ power of these models to characterize the strengths and weaknesses of school settings and assess a school’s fitness for implementing this particular intervention. Understanding a school environment prior to implementing a particular intervention will lead to fewer interventions that are incompatible with a school’s climate or are not sustainable.

Roxanne Moore is a Senior Research Engineer at the Georgia Institute of Technology in both Mechanical Engineering and at the Center for Education Integrating Science, Mathematics, and Computing (CEISMC). She received her PhD in Mechanical Engineering from Georgia Tech in 2012, which focused on modeling, systems engineering, and optimization. She now focuses primarily on the STEM pipeline, and has written engineering curriculum for middle and high school students. Her passion for improving access and equity in education has led her to apply systems modeling techniques to better understand educational environments and how to improve them.

Patrick McQuillan and Brad Kershner Urban School Leadership & Adaptive Change: The “Rabbit Hole” of Continuous Emergence

Abstract. In the current educational context deliberate and continuous emergence seems eminently logical. Schools comprise so many interacting dimensions—moving parts of people, ideas, contexts, and resources—change truly is the norm. School systems therefore need to adjust to both the challenges and opportunities they regularly encounter. In doing so, the principal represents a critical leverage point. Believing that “Leadership is no longer the activity of gatekeeping and directing but of enabling and empowering” (Morrison, 2002, p. 19), Elmore (2000) wrote that administrative leaders should “enhance the skills and knowledge of people in the organization [and] create a common culture of expectations around the use of those skills and knowledge” (p. 15). One strategy for addressing this challenge and enacting these ideals is to generate a complex adaptive system in which power and authority are decentralized and all school personnel—students, teachers, administrators, and parents—embrace a common vision committed to shared beliefs, values, policies, and practices. Accordingly, we conceptualize systems emergence as an adaptive process in which a school “system” adjusts to its context, drawing upon the analytic heuristic known as continuous emergence to reveal the ongoing and intertwining challenges that arise for urban school leadership when this occurs. In terms of the emergence process, we engage the experience of disequilibrium, intensification, emergent order, and stabilizing feedback not as linear phenomena leading to a single outcome but as an ongoing process in which these features of emergence interact in ways that are largely non-linear and unpredictable yet still reveal promising strategies for adapting to the varied sources of disequilibrium that arise in the system.

Patrick McQuillan an Associate Professor in the Lynch School of Education at Boston College, has a PhD in cultural anthropology from Brown University. He works extensively in urban schools and his current research interests focus on complexity theory and educational reform, with an emphasis on the role of the school principal in transforming urban schools. His publications include Reform and Resistance in Schools and Classrooms: An Ethnographic View of the Coalition of Essential Schools (Yale University Press, 1996; co-authored with Donna Muncey) and Educational Opportunity in an Urban American High School: A Cultural Analysis (SUNY Press, 1998). He recently (2014 and 2016) organized two symposia for the American Educational Research Association that were focused on understanding how complexity theory can inform school change.