Students use graph theory to create social graphs for their own social networks and apply what learn to create a graph representing the social dynamics found in a dramatic text. Students then derive meaning based on what they know about the text from the graphs they created. Students learn graph theory vocabulary, as well as engineering applications of graph theory.

Students analyze their social networks using graph theory. They gather data on their own social relationships, either from Facebook interactions or the interactions they have throughout the course of a day, recording it in Microsoft Excel and using Cytoscape (a free, downloadable application) to generate social network graphs that visually illustrate the key persons (nodes) and connections between them (edges). The nodes in the Cytoscape graphs are color-coded and sized according to the importance of the node (in this activity, nodes are people in students' social networks). After the analysis, the graphs are further examined to see what can be learned from the visual representation. Students gain practice with graph theory vocabulary, including node, edge, betweeness centrality and degree on interaction, and learn about a range of engineering applications of graph theory.

Students simulate disease transmission by collecting data based on their proximity to other students. One option for measuring proximity is by having Bluetooth devices "discover" each other. After data is collected, students apply graph theory to analyze it, and summarize their data and findings in lab report format. Students learn real-world engineering applications of graph theory and see how numerous instances of real-world relationships can be more thoroughly understood by applying graph theory. Also, by applying graph theory the students are able to come up with possible solutions to limit the spread of disease. The activity is intended to be part of a computer science curriculum and knowledge of the Java programming language is required. To complete the activity, a computer with Java installed and appropriate editing software is needed.

Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.

Graph theory is a visual way to represent relationships between objects. One of the simplest uses of graph theory is a family tree that shows how different people are related. Another application is social networks like Facebook, where a network of "friends" and their "friends" can be represented using graphs. Students learn and apply concepts and methods of graph theory to analyze data for different relationships such as friendships and physical proximity. They are asked about relationships between people and how those relationships can be illustrated. As part of the lesson, students are challenged to find the social graph of their friends. This prepares students for the associated activity during which they simulate and analyze the spread of disease using graph theory by assuming close proximity to an infected individual causes the disease to spread.

Students research a breed of poultry.  They share their information by creating a fake Facebook Page for their animal.  Both an electronic and paper-based form are included.

Students curate and create social media posts for a district/school company page. All resources are linked as Google Documents

Estimated Lesson Time: 45 minutes

Students will be able to:
-Understand how social media can influence spending habits
-Analyze marketing techniques from social media and advertising that encourage spending

ANSWER KEY LINKS: Create a Next Gen Personal Finance (NGPF) account to access answer keys. They will be listed under the Full Year Curriculum tab.

Diversity begets creativity - in this seminar we tap the amazing power of swarm creativity on the Web by studying and working together as Collaborative Innovation Networks (COINs). As interdisciplinary teams of MIT management, SCAD design, University of Cologne informatics, and Aalto University software engineering students we will explore how to discover latest trends on the Web, and how to make them succeed in online social networks. We study a wide range of methods for predictive analytics (coolhunting) and online social marketing (coolfarming), mostly based on social network analysis and the emerging science of collaboration. Students will also learn to use our own unique MIT-developed Condor tool for Web mining, social network analysis, and trend prediction.