Program
Session I: Education as Networks
8:30-8:50 Invited Talk: Adam Maltese (Indiana University, USA)
Using networks to analyze the academic persistence of undergraduates
8:50-9:05 Victor Landaeta-Torres1, Cristian Candia-Castro-Vallejos1,2, Carlos Rodriguez-Sickert1, Camilo Rodriguez Beltran1, Jorge Fábrega1, and César A. Hidalgo2 (1 UDD, Chile; 2 MIT, USA)
Does classroom cooperation promote learning?
9:05-9:20 Massimo Stella1, Mason A. Porter2, Hiroki Sayama3, Manlio De Domenico4, Sarah De Nigris5, Aleksandra Aloric6, and Vittorio Loreto7 (1 University of Southampton, UK; 2 UCLA, USA; 3 Binghamton University, USA; 4 Universitat Rovira i Virgili, Spain; 5 Ecole Normale Superieure de Lyon, France; 6 Petnica Science Center, Serbia; 7 Sapienza University of Rome, Italy)
Complex Forma Mentis: Building scientific links for understanding a complex world
9:20-9:35 Adam Maltese1, Ariel Zych2, and Kibeom Hong1 (1 Indiana University, USA; 2 Science Friday Initiative, USA)
#CephalopodWeek - Tracking a social media campaign related to science
9:35-9:50 Flávio L. Pinheiro1 and Sara Encarnação2 (1 MIT, USA; 2 CICS.NOVA-FCSH/UNL, Portugal)
Mapping the higher education system
(* Best contribution award winner *)
9:50-10:20 Keynote Talk: Katy Börner
(Network) Data Visualization Literacy
10:20-10:40 Coffee break & posters
Session II: Education on Networks
10:40-10:55 Sahana Giridharan1,2 and Robin W. Wilkins2,3,4 (1 The Early College at Guilford, USA; 2 Network Neuroimaging Laboratory for Complex Systems, USA; 3 Joint School of Nanoscience and Nanoengineering, USA; 4 Gateway MRI Center, University of North Carolina Greensboro, USA)
A high school student's experience researching music and brain networks
10:55-11:10 Takaaki Aoki (Kagawa University, Japan)
Teaching network science as a faculty of education in Japan
11:10-11:25 Stephen Uzzo, Catherine Cramer, and Michaela Labriole (New York Hall of Science, USA)
Highlighting network literacy opportunities in NGSS: Pathways connecting essential concepts with disciplinary core ideas
(Canceled) 11:25-11:40 Paul Trunfio (Boston University, USA)
Creating, sustaining and scaling partnerships to broaden the impact of network science education
11:40-11:55 Toshihiro Tanizawa (National Institute of Technology, Kochi College, Japan)
Network science in your pocket
11:55-12:10 Emma Towlson (Northeastern University, USA)
¿Six degrees of separación?: Experiences from designing and implementing an intensive, interdisciplinary, and project-based network science course in Guadalajara
12:10-12:30 Invited Talk: Aaron Clauset (University of Colorado at Boulder, USA)
Computational thinking and the pedagogy of network science
Posters
Ewa Sulicz1, Joyce Zhu1, Evan George1, Kashaf Nadeem1, Alexis VanDonsel1, Sheng-Liang Slogar1, Carol Reynolds1, and Hiroki Sayama2 (1 Vestal High School, NY, USA; 2 Binghamton University, USA)
How behavioral attributes affect the cohesiveness of society: An agent-based social network simulation
Bethany Sanov1, Sebastian Sanchez1, Avery Sanov1, Michelle Lovett1, and Robin W. Wilkins2 (1 Southwest High School, NC, USA; 2 University of North Carolina at Greensboro, USA)
A NetSci secondary student cohort's presentation on music and brain networks research
Time allocation (including Q&As):
Keynote talk: 30 mins.
Invited talks: 20 mins.
Regular talks: 15 mins.
Abstracts and Slides
Keynote Talk:
Katy Börner
(Network) Data Visualization Literacy [Slides]
This talk presents the results of a recent study that examined the “data visualization literacy” of over 900 youth and adult visitors across six U.S. science museums. Results show that: a very high proportion of the population, both adult and youth, cannot interpret data visualizations beyond very basic reference systems; construction of complex visualizations led to more accurate interpretation than deconstruction; and individuals are willing to spend time attempting to make meaning in representations depending on their personal interest in the topic. I then discuss a theoretically grounded and practically useful visualization framework that was developed to empower the broadest spectrum of users to read and make data visualizations that are useful and meaningful to them. The visualization framework was used to conduct the aforementioned study and is used to develop plug-and-play macroscope tools that improve the (network) data visualization literacy of researchers, practitioners, IVMOOC students, museum visitors, and others.
Invited Talks:
Adam Maltese (Indiana University, USA)
Using networks to analyze the academic persistence of undergraduates [Slides]
In this talk I will explain how we are attempting to use networks to understand more about the persistence and major choices of undergraduates. While there is research suggesting that friends are influential in various aspects of student life (e.g., support, class choice) it is not clear if these effects would manifest when modeling student persistence and major choice. In this investigation we are using institutional records for students at our university to explore these issues. After a number of false starts we seem to finally be making progress in analyzing parts of the data. I will share some of our experiences related to this exploration, initial findings and lessons we are learning.
Aaron Clauset (University of Colorado at Boulder, USA)
Computational thinking and the pedagogy of network science [Slides]
Modern network science is a broad and interdisciplinary field, but most students in the field have a background in a particular discipline. Moreover, despite its interdisciplinary aspirations, research in network science is increasingly disciplinary. For instance, computer scientists have their own approach to networks, which is different from that of physicists, which are both different from mathematicians, etc. Bridging these differences poses unusual pedagogical challenges for teaching network science. What topics should be covered? In what way should they be covered? How should evaluations be structured? What are the key learning goals? In this talk, I will describe my perspective on the challenges of teaching this interdisciplinary subject within the disciplinary environment of a Computer Science department. I will describe lessons learned about what has worked and not worked, about how attitudes, preferences, and skills vary across students from different communities, and what I think are some key learning goals for training the next generation of network scientists. I'll close with some forward looking thoughts about the utility of focusing on computational thinking in network science training.
Contributed Talks:
Victor Landaeta-Torres1, Cristian Candia-Castro-Vallejos1,2, Carlos Rodriguez-Sickert1, Camilo Rodriguez Beltran1, Jorge Fábrega1, and César A. Hidalgo2 (1 UDD, Chile; 2 MIT, USA)
Does classroom cooperation promote learning? [Slides]
Does classroom cooperation promote learning? The literature on social learning has shown that people are more likely to learn from those who are seen as prestigious, talented, or that share demographic attributes with learners. Yet, the connection between cooperation and learning is relatively understudied. Here, we explore the connection between student performance and classroom cooperation by mapping six classrooms networks using a non-anonymous dyadic cooperative game. In our game, a variation of the prisoner’s dilemma, in every round students are endowed with tokens that they can share or keep (cooperate or defect). The total number of tokens that a student gets is equal to the number of tokens they kept plus twice the number of tokens they received. Hence, the group maximizes the total number of tokens earned when everyone cooperates, but students maximize their tokens when they defect and everyone else cooperates. We use this game to map a weighted network of cooperation for each classroom, with weights equal to the amount of tokens received by each student in each dyadic game. Finally, we compare the centrality of each student with their classroom grades (GPA) and find a positive and statistically significant relationship between network centrality, measured as the sum of tokens received, and a student’s academic performance. These results suggest a link between cooperation and learning and open new avenues for the role of networks in education.
Massimo Stella1, Mason A. Porter2, Hiroki Sayama3, Manlio De Domenico4, Sarah De Nigris5, Aleksandra Aloric6, and Vittorio Loreto7 (1 University of Southampton, UK; 2 UCLA, USA; 3 Binghamton University, USA; 4 Universitat Rovira i Virgili, Spain; 5 Ecole Normale Superieure de Lyon, France; 6 Petnica Science Center, Serbia; 7 Sapienza University of Rome, Italy)
Complex Forma Mentis: Building scientific links for understanding a complex world [Slides]
Increasingly, academic and industrial research is going beyond a “reductionist” paradigm and employs ideas both from multiple disciplines and from complex systems. However, in schools, STEM disciplines are still taught in a reductionist way, suffering from a negative and fragmented perception, whose onset starts already in high school. So far, this strong compartmentalization between STEM subjects has not been specifically quantified at a large scale. It is therefore important to characterize the impact of such fragmentation, in order to improve the understanding and appreciation of STEM subjects on the long run.
The research-outreach hybrid project we propose, “Complex Forma Mentis”, aims at such endeavour: quantifying how high school students, teachers and early-stage researchers associate and perceive scientific concepts by free associations (e.g. “Physics” reminds of “Nature”). Networks of these conceptual associations represent a quantitative proxy of the subjects’ knowledge structure about science. This networked representation allows to identify educational issues about both scientific content (e.g. students missing associations present in teachers/researchers) and wrong attitude towards science (e.g. significant clustering of negatively perceived concepts). Complex Forma Mentis also aims at using this knowledge representation for better planning long-run activities exposing high-school students to relatable complex patterns, such as game-theoretic dilemmas or small-worlds in social networks, among many others.
For the data gathering and the long-term activities, it is necessary to build a network of collaborations with school. In order to achieve this in the short run, the project offers also seminars on complexity given by young researchers on a voluntary basis to schools. Seminars also aim at showing how math and physics can work in synergy with other disciplines for understanding real-world systems (e.g. crowd behaviour can be predicted with statistical mechanics).
Adam Maltese1, Ariel Zych2, and Kibeom Hong1 (1 Indiana University, USA; 2 Science Friday Initiative, USA)
#CephalopodWeek - Tracking a social media campaign related to science [Slides]
Evidence exists that twitter communities can generate and support informal learning for individual users in various learning communities. Clarifying the roles of different participants in spontaneous and planned Twitter learning communities is the first step towards improving their utility as a method of science communication and outreach.
The central focus of this work is to determine the true spread of the #CephalopodWeek campaign. Specifically, we focus on: 1) Who was involved? 2) What content was shared? 3) Was the spread of information multidirectional or only outward from key sources?
We collected tweets through three separate sources: a) an If This Then That (IFTTT) recipe that monitored twitter for anything using the hashtag #CephalopodWeek, b) a Python script to snag tweets including #CephalopodWeek, c) the NCapture tool from NVivo to pull tweets from the twitter search for #CephalopodWeek at various points during the campaign. We combined the tweet IDs captured through each source and kept only unique values, which resulted in a total of 13065 tweets using the #CephalopodWeek tag between June 17-30, 2016. We next used Python to pull the relevant information for each one of these tweet IDs, which created the corpus of data we used for analysis. Additionally, we used Truthy (truthy.indiana.edu) to create network graphs and to identify the co-occurrence of hashtags used along with #CephalopodWeek.
Of the tweet corpus, 18.5% were original posts. The majority of the tweets came from the US coasts - NYC-area dominated, followed by the San Francisco/Bay Area and Washington, D.C. In looking at top hashtags during the campaign were: Octopus (710), squid (385), FossilFriday (248), 頭足類週間 (Japanese for Cephalopod week; 234) and nautilus (166). As this analysis advances, we will advance our network analysis to study interrelationships between organizations and individuals sharing scientific information through this campaign.
Flávio L. Pinheiro1 and Sara Encarnação2 (1 MIT, USA; 2 CICS.NOVA-FCSH/UNL, Portugal)
Mapping the higher education system
Comparable statistical data is of fundamental importance for policy making on Higher Education. Current classification schemes of education fields that support data production are based on the UNESCO’s International Standard Classification of Education - ISCED. This scheme focus on the comparability of educational programs and as such of different majors. However, this does not always overlap with the similarity between majors when one takes into account the students’ choices when applying to Higher Education. Here, we study the network of similarities between majors using data of the applicants to the Portuguese Public Higher Education System between the years of 2008 and 2015 and containing 380.375 applications. The network is structured around 8 communities. The internal composition of each community shows that there are new potential complementarities within institutions that should be weighted in policy making and higher education management. Furthermore, we have found that while gender is a determinant factor, in the structure under analysis, it constrains the employment opportunities of candidates since it exhibits a strong pattern of assortment in relation to unemployment levels Future research concentrates in understanding the range of phenomena captured by this structure and how it impacts the opportunities of graduates in the labor market.
Sahana Giridharan1,2 and Robin W. Wilkins2,3,4 (1 The Early College at Guilford, USA; 2 Network Neuroimaging Laboratory for Complex Systems, USA; 3Joint School of Nanoscience and Nanoengineering, USA; 4 Gateway MRI Center, University of North Carolina Greensboro, USA)
A high school student's experience researching music and brain networks
As a high school student interested in studying music and the brain using computational neuroscience, I found a unique opportunity to be a lab research assistant in the Network Neuroimaging Lab. Starting with MRI training videos and reading brain networks papers, I began to understand the basics of brain connectivity using network science methods. Using the CONN toolbox to analyze basic brain connectivity served as the starting point of my network science education journey. Shortly after, I used previously collected data to write a paper on analyzing the effects of preferred music on connectivity between two brain regions. After presenting my work at various research symposiums, I continued to read papers on graph theory while learning how to perform UNIX/Linux command line scripting and work in MATLAB. After running practice neuroimaging datasets for network science analyses—using the GraphVar toolbox—I started working with ‘real’ brain data on an IRB approved study measuring changes in functional brain networks based on emotional responses to music stimuli. From organizing the fMRI data for the toolbox to running GraphVar myself and analyzing the network metrics, I learned every step of the lab’s data processing stream for network neuroscience analyses of functional brain connectivity. Learning and applying these network science methods and analyses techniques in a research setting and working with ‘real’ data has well equipped me to pursue further network neuroscience research studies and explore future possibilities in interdisciplinary network science fields.
Takaaki Aoki (Kagawa University, Japan)
Teaching network science as a faculty of education in Japan [Slides]
In this talk, I would like to talk about teaching network science in a faculty of education in Japan. The most students have no background of physics and mathematics and little skills of computers. In my course of the faculty, they learn mainly humanities and social sciences and study fieldworks of interviews and surveys, that aims to improve the modern day problems found within internationalization, the digital age, and other challenges facing society. The issues in this talk are as follows:
How to make the student to be interested in network science?
What do we should teach as a literacy of network science to those students?
I have a classroom to teach network science in the last 6 years from 2011. I will report several attempts on the issues tried in the classroom, in which the addressed topics and the examples of networks are related to the social relationships that are familiar to them. Moreover, if I have enough time, I will introduce a new method of social survey using an idea of community detection developed in network science, which is collaborated with Tetsuro Kawamoto and Harumi Tokioka.
Stephen Uzzo, Catherine Cramer, and Michaela Labriole (New York Hall of Science, USA)
Highlighting network literacy opportunities in NGSS: Pathways connecting essential concepts with disciplinary core ideas [Slides]
Young people intuitively understand and use network thinking in their daily lives, yet there is currently no formal educational support for, or inclusion of network literacy at pre-K to grade 12 levels. There is an urgent need to address this deficit in order to prepare today’s students to be active, productive, and engaged citizens, professionals and policymakers in 21st century society. The most current science teaching standards – Next Generation Science Standards or NGSS – are notable for structural innovations, employing, for example, Disciplinary Core Ideas (DCIs) and Cross-Cutting Concepts, yet teachers find NGSS difficult to implement in their classrooms. We will discuss how Network Literacy can help support the shifts in thinking that NGSS requires, both in understanding and using NGSS.
For teachers integrating NGSS, network literacy is useful as a process, and as a bridge to the next steps in curriculum development, enabling interdisciplinary connections and stimulating higher order and analytical thinking about existing areas of study. We will present examples of how a network literate teacher is able to look at processes, not just pieces; use a network approach to teach concepts that are “hard to teach”; and can approach classic pedagogical visuals such as the Periodic Table to reveal more meaningful relationships and patterns. We will discuss how mapping network literacy essential concepts to NGSS standards helps make abstract concepts more meaningful and useable.
Toshihiro Tanizawa (National Institute of Technology, Kochi College, Japan)
Network science in your pocket [Slides]
Thinking that the accomplishments of Network Science have large influences on our everyday life, the basic knowledge of Network Science deserves to be included in basic curricula at every level of educational systems from kindergarten to undergraduate. The concept of “Network Literacy” embodies this situation. To make this concept into action, various kinds of lecture courses in different educational levels have to be provided. To prepare even a single lecture course on network science, instructors have to deal with a large amount of different kinds of materials, such as images, charts, and documents. If the course contains hands-on activities, various programming environments for C, C++, Java, Python, etc., also have to be provided. Connection to the Internet is also preferable, since students may look up various technical terms online during the class and the instructor may also use online contents during a lecture. These issues become particularly challenging in educational outreach settings, where the instructor needs to give a lecture at elementary or secondary schools outside her home campus. Under such circumstances, all the course materials have to be made portable, together with the programming environments and Internet connections. In this talk, I report an initiative, named "Network Science in Your Pocket", to construct completely portable hardware/software environments for off-campus educational outreach of network science, which was made possible by recent advancement of tiny one-board computer technology. A sample project for building such environments and course materials is described and a case study in an open-house outreach course given at a technical college in Japan is presented. If possible, an little on-site demonstration using a real “tiny server” will be given.
Emma Towlson (Northeastern University, USA)
¿Six degrees of separación?: Experiences from designing and implementing an intensive, interdisciplinary, and project-based network science course in Guadalajara [Slides]
Network Science is a very young field, which has seen an explosion of interest in the past two decades, and from all corners of the disciplinary spectrum. As such, there is a relative deficit in educational material and resources for training in the subject. Indeed, it is only recently that dedicated PhD programs and sustained efforts to infiltrate the University and High School curricula are emerging. This gap, existing especially for students and researchers from disciplines not traditionally associated with complex systems, has led to an increasing demand for intensive short "crash courses" in the main tools for and rigorous principles behind Network Science based approaches.
Based on the graduate level course "Introduction to Network Science" I co-instruct at NEU, which is firmly rooted in pedagogical ideology, we designed a summer course for deployment at a highly innovative start-up lab with special interests in the social sciences at ITESO in Guadalajara. I will discuss the challenges and opportunities we encountered in working with both undergraduates and researchers, making the material accessible for individuals with a wide variety of backgrounds, and maximising the impact of state-of-the-art technology. I will describe our ethos and approach, and which components succeeded the best and least well, and the lessons learned for repeating and expanding upon similar endeavours in the future.