Program, Abstracts, and Slides

Acquiring Knowledge, Insight, and Skills through Network Modeling and Inquiry

Chris Arney

Director of Interdisciplinary Modeling, COMAP, USA

Professor Emeritus, United States Military Academy, West Point, NY

[Slides]

One of the fundamental issues in education is determining when a learner is ready to transition from simple constructs to more complex and interdisciplinary models. Putting this another way, when is the mind ready to switch from linear, fact-based knowledge to nonlinear, relationship-based knowledge. Related to these issues is the need to determine when a learner is ready to transform her or his knowledge into higher level skills such as creating insight, building wisdom, and performing planning and decision making. More critical for modern education is determining what roles modeling and inquiry play in the development of the skills of understanding, confronting, and embracing complexity. This presentation provides several constructs and examples using the basic principles of network modeling (e.g., relationships, patterns, metrics, environmental effects) to teach problem solving, modeling, inquiry, and the elements of complexity at several levels of school education. These teaching and learning processes are mostly geometric and make use of graphs and networks, dynamic frameworks, and mental maps to visualize concepts and to motivate the teacher and learner. Learning is a complex process that requires appropriate tools and robust teaching algorithms to help all learners prepare for the tasks of the modern world.

Network Science Education at Northeastern University

Kate Coronges

Northeastern University, USA

The graph-tool Python Module for Network Analysis

Tiago P. Peixoto

University of Bath, UK

Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs and networks. It is designed to harness the ease of use and expressiveness of the Python language together with computationally efficient core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library, while at the same time being easy to learn and comfortable to use like regular Python libraries. In this talk I will give an overview of the tool, describing its design patterns and common uses.

The GitHub Project, "Network Science in Your Pocket"

Toshihiro Tanizawa

National Institute of Technology, Kochi College, Japan

Since the first idea presented in NetSciEd5 at Seoul in 2016, I have been building a concrete example of "Network Science in Your Pocket", which is a project that enables teachers to give an series of lectures on various topics of network science at any place even outside campus with tiny one-board computing devices. Since the one-board computing device has now become a common technology, the importance of a rich amount of contents for various educational purposes becomes much more important than ever. In this talk, I will describe a prototype examples for interactive Web contents for the Project which is going to be uploaded on GitHub for those who are interested in this project to use as a starting point for making their own contents.

Assessing, Improving, and Evaluating Interdisciplinary Teaching Practice Using

Bipartite Network Analysis

Carrie Diaz Eaton

Bates College, USA

Henderson, Beach, and Finkelstein reviewed STEM education reform strategies in 2011 with particular attention on teaching practice intervention. These interventions typically operate on two axes: intervening at the environment or the individual and using prescribed or emergent interventions. In this article, we explore the use of network analysis to inventory and assess interdisciplinary teaching practices including interdisciplinary curriculum development as a result of interventions on individual instructors. In this context, we examined the evolution of teaching practice in a marine biology classroom during a collaborative period between a mathematician and marine biologist on interdisciplinary education. We adopt an analysis toolbox for ecologically mutualistic bipartite networks and apply it to understand relationships between mathematical and marine biology concepts. In particular, we discuss the use of bipartite network analysis to understand and improve interdisciplinary teaching practice. Lastly, we connect these techniques and visualizations to insights about assessing, improving, and evaluating teaching practice and curriculum development.

Using Bipartite Networks to Understand Student Enrolment Patterns through High School in New Zealand

Steven Turnbull

University of Auckland, New Zealand

[Slides]

The trajectories that students take through high school can be highly differentiated and complex. This is particularity true in the case of New Zealand, where schools and teachers are offered a lot of flexibility in the way in which they assess students and structure courses. Due to this flexibility, the main New Zealand high school qualification, NCEA (National Certificate of Educational Achievement), has been described as one of the most complicated assessment systems in the world (Hipkins, 2016). In her book “NCEA in Context”, Rosemary Hipkins calls for researchers to reimagine NCEA as a complex system. The following talk will discuss a network analysis approach that follows this call. More specifically, we will discuss how bipartite networks, combined with a method of normalisation called Revealed Comparative Preference (RCP), can be used to reveal patterns of courses that tend to be taken together by students. In doing so, we can unpack the complexity of NCEA and get an idea of how the New Zealand education system serves its students.

We make use of an internationally unique data infrastructure called the Integrated Data Infrastructure (IDI), to analyse student enrolment patterns for the whole student population of New Zealand. The IDI is a collection of datasets (education, census, tax records etc.) linked at an individual level. The IDI allows us to consider how important demographic attributes (such as gender, ethnicity, parental education and deprivation level) relate to the pathways that past students took through NCEA. Our research specifically focuses on 9 separate birth cohorts (from 1991-1998), with the aim of outlining trends in Science, Technology, Engineering and Mathematics (STEM) course selection in NCEA, and how this differs at the intersection of student gender, ethnicity and social class.

Generating Meaningful Collaboration Networks in Complex Systems Education

Juniper Lovato

University of Vermont, USA

In this talk, we will discuss unconventional generative processes and social reinforcement methods that my team has used to create meaningful learning experiences during research-focused schools, workshops, working groups, and short courses. We will explore ways to support and encourage creative trans-disciplinary research collaborations, build strong institutional partnerships, and create an impactful and supportive experience for participants. We will go through several examples of partnerships and programs that were successful or unsuccessful and discuss what elements heightened or hindered their efficacy. The goal of this talk is to challenge future organizers to build a strong network of partnerships, to think beyond the customary program format, and to create human-focused research processes.