Secondary Student Mentorship and Research in Complex Networks: Process and Effects

Abstract

There is increasing interest in rethinking approaches to K-12 education that better prepare students to face an adult world and work life of data-driven and interdisciplinary science, technology, engineering, and mathematics (STEM). The science of complex networks, also known as network science, is the application of advanced graph theory to characterize, visualize, and analyze complex connected social, biological, technological, and physical systems. It is an important approach to study many problems in data-driven STEM and provides an intuitive pathway for students of any age to understand complex systems. This paper describes the development of a successful mentorship model that combines deep engagement with team research, enabling high school students and teachers to perform successful research projects in the science of complex networks.

Appendices

Appendices

1.1 NetSci High Student Research Projects 2010–2015

2010–2011

• A Comparative Study on the Social Networks of Fictional Characters

• Academic Achievement and Personal Satisfaction in High School Social Networks

• Does Facebook Friendship Reflect Real Friendship?

• Inter-Species Protein-Protein Interaction Network Reveals Protein Interfaces for Conserved Function

• The Hierarchy of Endothelial Cell Phenotypes

• Preaching To The Choir? Using Social Networks to Measure the Success of a Message

• Identification of mRNA Target Sites for siRNA Mediated VAMP Protein Knockdown in Rattus norvegicus

2011–2012

• A Possible Spread of Academic Success in a High School Social Network: A Two-Year Study

• Research on Social Network Analysis from a Younger Generation

2012–2013

• Interactive Simulations and Games for Teaching about Networks

• Mapping Protein Networks in Three Dimensions

• Main and North Campus: Are We Really Connected?

• High School Communication: Electronic or Face-to-Face?

• An Analysis of the Networks of Product Creation and Trading in the Virtual Economy of Team Fortress 2

2013–2014

• A Network Analysis of Foreign Aid Based on Bias of Political Ideologies

• Comparing Two Human Disease Networks: Gene-Based and System-Based Perspectives

• How Does One Become Successful on Reddit.com?

• 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

2014–2015

• Network Analysis of Microgravity-Influenced Genes in Salmonella enterica serovar typhimurium

• Connecting Radon Levels to Cancer rates in California Counties: A Network Approach

• National Football League Network

• Drug combinations and adverse side effects

• Comparing post-secondary institutions across the United States

• Relationships Between Musculoskeletal System and High School Sports Injuries

• Similarities Found in Neurological Disorders Based on Mutated Genes and Drug Molecules

• The Relationships of International Superpowers

• Protein Association and Nucleotide Similarities Among Human Alpha-Papillomaviruses

1.2 Network Literacy: Essential Concepts and Core Ideas

• 1. Networks are everywhere.

  • The concept of networks is a broad, general idea about how things are connected and working together. Networks are present in every aspect of life.

  • There are networks that form the technical infrastructure of our society (e.g., communication systems, the Internet, the electric grid, the water supply).

  • There are networks of people, e.g., families and friends, email/text exchanges, Facebook/Twitter/Instagram, and professional groups.

  • There are economic networks, e.g., financial transactions, corporate partnerships, and international trades.

  • There are biological/ecological networks, e.g., food webs, gene/protein interactions, neural networks, and spread of diseases.

  • There are cultural networks, e.g., language, literature, art, history, and religion.

  • Networks can exist at various spatial and/or temporal scales.

• 2. Networks describe how things connect and interact.

  • There is a subfield of mathematics that applies to networks. It is called graph theory. A graph in mathematics means a network.

  • Connections are called links, edges, and ties. Things that are connected are called nodes, vertices, and actors.

  • Connections can be undirected (symmetric) or directed (asymmetric).

  • The number of connections a node has is called a degree of that node.

  • In some networks, you can find a small number of nodes that have much larger degrees than others. They are called hubs.

  • A sequence of links that leads you from one node, through other nodes, to another node is called a path.

  • In some networks, you can find a group of nodes that are well connected to each other. They are often called clusters, cliques, and communities.

• 3. Networks help reveal patterns .

  • You can represent something as a network by describing what its parts are and how they are connected to each other. Such network representation is a very powerful way to study its properties.

  • Some of the properties in a network that you can study are:

    • How the degrees are distributed across nodes.

    • Which parts or connections are the most important ones.

    • Strengths and/or weaknesses of the network.

    • If there is any substructure or hierarchy.

    • How many hops, on average, are needed to move from one node to another within the network.

      Using these findings, you may be able to make predictions.

• 4. Visualizations can provide an understanding of networks.

  • Networks can be visualized in a number of different ways.

  • You can draw a diagram of a network by connecting nodes with links.

  • There are a variety of tools available for visualizing networks.

  • Visualization of a network often helps to understand it and communicate the ideas to people in an intuitive, nontechnical way.

  • Creative information design plays a very important role in making an effective visualization.

  • It is important to be careful when interpreting and evaluating visualizations, because they may not tell the whole story about the networks.

• 5. Today’s computer technology allows us to study real-world networks.

  • Computer technology has dramatically enhanced our ability to study networks, especially large complex ones.

  • There are many free software tools available for network visualization and analysis.

  • Using personal computers, everyone can easily model, visualize, and analyze networks, not just scientists.

  • Through the Internet, everyone has access to many interesting network data.

  • Computers allow us to simulate hypothetical or virtual networks, as well as real ones.

  • Learning basic computer literacy skills opens the door to infinite possibilities, e.g., file/folder operation, data entry, manipulation and modeling, information sharing and collaboration, and computer programming.

• 6. Networks help us compare a wide variety of systems.

  • Various kinds of systems, once represented as networks, can be compared to see how similar or different they are.

  • Certain network properties commonly appear in many seemingly unrelated systems. This implies that there may be some general network principles across disciplines.

  • Other network properties are quite different from systems to systems. These properties can help classify networks in different families and understand them differently.

  • Science has traditionally been conducted in separate disciplines. Networks can help go beyond disciplinary boundaries toward a more cross- or interdisciplinary understanding of the world.

  • Networks can help transfer knowledge from one discipline to another to make a breakthrough.

• 7. Network structures can influence their dynamics and vice versa.

  • Network structure means how parts are connected in a network.

  • Network dynamics means how things change over time in a network.

  • Network structures can influence their dynamics. Examples include the spread of diseases, behaviors or memes in a social network, and traffic patterns on the road network in a city.

  • Network dynamics can influence their structures. Examples include the creation of new following links in social media and construction of new roads to address traffic jams.

  • Network structures and dynamics often influence each other simultaneously.

1.3 Outreach Events

• NetSci High has facilitated sending a group of high school students and teachers from New York City to NetSci 2011 in Budapest, Hungary; a group from Endwell and Vestal, NY to NetSci 2012 in Evanston, IL; and a group from Vestal, NY to NetSci 2014 in Berkeley, CA. In all of these travels, the high school student teams presented their work at poster sessions. High school student research has also been published in peer-reviewed journals such as PLOS One (Blansky et al. 2013).

• Students and teachers from Newburgh Free Academy and Vestal High School presented posters at the IEEE ISEC conference at Princeton University in 2015 and 2016. NetSci High participating students and teachers have presented at the West Point Cadet Seminar on Network Science each year of NetSci High.

• In 2016 NetSci High students and teachers participated in the US Science and Engineering Festival in Washington, D.C., presenting several hands-on activities related to the Network Literacy Essential Concepts.