Data & Network Science in K-20 Education

Data & Network Science in K-20 Education @ BU aims to bring data, network, complexity and systems science literacy to a sustainable “K-20” STEM (Science, Technology, Engineering and Math) + Art education pipeline of innovators in research and education.

NetSci High has been our first leap in this pursuit, immersing high school students and teachers in the burgeoning field of network science, a core pathway to making sense of many kinds of Big Data. Each year, NetSci High begins with an intensive residential summer workshop using a network lens to understand and find solutions to complex social, health and environmental problems. Students and teachers are introduced to network science foundations including graph theory, statistical inferencing, data mining, systems theory, and information visualization. We teach computational skills for visualizing and analyzing data using Gephi, Python, Javascript D3, and Processing. Perhaps most importantly, we motivate, inspire and cultivate creativity through team-building activities, mini-projects, integrating STEM with art and design, non-technical interactive talks by leading researchers, public speaking opportunities, and more. We tear down the walls between student, teacher and researcher. Students thrive in this environment, expanding their potential, and by the end of our workshop, student teams are armed with tools — and confidence — to embark on a year-long journey of independent discovery.

During the academic year, graduate student mentors from partner research labs together with high school teacher mentors guide the student teams in developing their research project. Students engage in data collection, data mining, data processing, and computational network modeling and analysis to discover answers to their specific research questions. The projects represent the interdisciplinary nature of network science and its ability to draw students of all interests into STEM fields. Student-driven research projects include:

  • A Network Analysis of Foreign Aid Based on Bias of Political Ideologies
  • Comparing Two Human Disease Networks: Gene-Based and Symptom-Based Perspectives
  • Influence at the 1787 Constitutional Convention
  • Quantifying Similarity of Benign and Oncogenic Viral Proteins Using Amino Acid Sequence
  • Quantification of Character and Plot in Contemporary Fiction
  • RedNet: A Different Perspective of Reddit
  • Tracking Tweets for the Superbowl

Student teams culminate their year-long experience through joint activities with the next class of students at the summer workshop. Student teams present their research projects in a conference setting, offer personal tips to new student teams, with keynote lectures by leading experts in the network science community.

[NetSciHigh] has opened doors for me that would not be open without the program. I have continued to work with computer science as well as network science. I am currently working with [grad student] to map out the recent Ebola outbreak in West Africa. Thank you once again and I hope to hear from you soon. – J.I., member of 2013/14 New York metro area team

The NetSci High program has garnered scholarships for student participants, fostered student-authored publications in peer-reviewed journals, and supported student teams in presenting research posters at the International NetSci conferences in Budapest, Hungary; Chicago, Illinois; Berkeley, California; and New York City. We look forward to furthering data and network science literacy through scaling student research opportunities, broader teacher training, and connecting K-12 to undergraduate education, science research and public outreach.

These efforts are funded by the National Science Foundation through the Innovative Technology Experiences for Students and Teachers program (ITEST) (Awards 1949526 and 1949484). Previous funding from the National Science Foundation (Award # 1139478/1139482) and the Cyber-enabled Discovery and Innovation program (CDI) and the Office of International Science and Engineering (OISE) (Award # 1027752)