SENTRY/CCICADA Education Modules

SENTRY Courses and Materials

SENTRY through a subaward to CCICADA develops homeland security materials and courses that are distributed to tens of thousands of students and practitioners nationwide. These modules are based on research conducted by SENTRY and CCICADA and other DHS Centers of Excellence. They are classroom-tested, widely used, and cover five to eight days of instruction. As noted in some modules, there will be professional development modules on CANVAS for the modules that can be used in the first two years of undergraduate courses. This Canvas site will be available by the end of 2023.

Sentry modules are accessible on both the CCICADA and Sentry websites and with accompanying professional development on Canvas and through pressbooks.

They can be accessed on Canvas Commons and imported into a Canvas course: https://lor.instructure.com/resources/8bbaae8defe2485c86f04ecfcadf8a97?shared

They can also be accessed as an eBook, and linked to from any learning management system or syllabus: https://uen.pressbooks.pub/sentry2023/

Sentry  Modules:


AI: A Brief Historical Introduction and Guide to Getting it to Work Right by James Kupetz and Karl Levy. Artificial Intelligence (AI) refers to machines or systems that can perform tasks that would typically require human intelligence. These tasks include problem-solving, understanding language, recognizing images, making decisions, and even learning from experience.Download full module


Big Blow Can Blow the Budget by Diana Cheng, Towson University, and Angelyn Flowers, University of the District of Columbia. This module is a mathematical and quantitative literacy activity on interpreting data. This activity examines information from the Federal Emergency Management Agency’s National Risk Index tool, the National Oceanic and Atmospheric Administration’s Historical Hurricane Tracks tool, and the Natural Hazards Research and Applications Information Center’s Children and Disasters Special Collection of research.
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Block Maximum Techniques and River Flooding by Brian Birgen, Wartburg College, Melanie Brown, Champlain College, and Joyati Debnath, Winona State University.The mathematical concept of Risk Assessment is fundamental in our everyday life. Almost every situation can be observed as some amount of risk involved. With an increase in extreme weather events, there is also an increased risk of property damage and loss of life. This module introduces students to Block Maximum Techniques in Statistics through the lens of river flooding. This particular application also reinforces the idea of a 100-year flood and 500-year flood to help students better understand the intersection of risk and probability in their own communities. Specific class activities are suggested to be performed that will help students become familiar with the risk assessment involved with river flooding. We also include instructions for doing the data analysis in Excel, R, and Minitab.
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Decision Trees in Action: Evacuation and Fire Safety by Donna Beers, Simmons University, and Clifton Morrow, Taylor Business Institute. The goal of this module is to introduce undergraduates across a variety of disciplines to the vital field of artificial intelligence and machine learning. This module introduces early undergraduates to an essential area of mathematics, the decision tree algorithm, which is the foundation of some of the most widely used classification algorithms (ham or spam? cat or dog? fire or no fire?) in machine learning today. In this module, students learn how to use basic logic to build elementary decision trees, essentially resembling a series of “yes/no” questions, that progressively split data into smaller subsets based on specific features, ultimately leading to a final prediction at the leaf nodes. For this reason, simple decision trees are easy to understand and interpret, making them accessible to students in elementary college mathematics courses. This module challenges students to simulate a crowded venue, track how civilians depart after an alarm, and develop plans to deploy first responders in case of natural or manmade threats. Specifically this module predicts which exit each civilian will use, so first responders could make triage plans. Overall this module will equip students to advance the safety and security of their communities.
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Network Security and Artificial Intelligence by Laura Watkins, laura.watkins@gccaz.edu, Glendale Community College, AZ, AMATYC. Artificial Intelligence (AI) can play a crucial role in improving network security. In today’s digital environment where cyber threats are becoming increasingly sophisticated and pervasive, AI can enable faster and more accurate threat detection due to its ability to rapidly analyze and interpret large volumes of network data and identify unusual patterns. As a result, AI can identify potential threats before significant damage is caused and free up human resources to focus on more complex issues.
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Exploring Functions Using Machine Learning Concepts by Laurel E. Clifford, Mohave Community College. The use of artificial intelligence (AI) is increasing exponentially in education, professional fields, security, and popular culture. As our society embraces this tool, we need to understand what AI really is: a tool making predictions from data, models, and algorithms and not actual human intelligence.
Understanding how machine learning trains computers to predict from data can help us make sense of AI and consider its limitations and ethical implications. While much of the mathematical computation behind machine learning may be beyond the precalculus level, the fundamental ideas are accessible to precalculus students and may motivate further study in STEM and AI fields. This module, written for use in an introductory unit early in the precalculus/college algebra course curriculum, explores function concepts and function classification through the lens of machine learning.
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Artificial Intelligence Educational Module – Modeling Sea Level Rise by Diana Cheng, Xiaoyin Wang, Chelsea McClure, Towson University, with input on lesson design from Brad Chin (West Valley Community College) & John Gonzalez (US Dept of Defense).
Artificial intelligence can improve mathematics educators’ effectiveness in their teaching, but many have not yet learned how to leverage it as a useful educational tool. Our research question is, “what are inservice teacher perceptions towards the integration of Artificial Intelligence in their classrooms?” Based on Chelsea McClure’s previous experiences with the integration of AI into SCED 554: Secondary Methods / Humanities for the Master of Arts in Teaching program, we aim to collaborate to develop a similar project that will be used in two Master of Science in Mathematics Education graduate courses offered in Fall 2024 which Diana will teach. The innovative classroom activity will ask teachers to complete several projects with the assistance of Artificial Intelligence, including adapting and improving learning materials developed to teach social justice mathematics lessons and designing assessments.
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An Introduction to Centrality Measures with a Transportation Network Defense Exercise by James Kupetz, Jr. and Karl Levy, Ph.D. A graph (G) is a set of vertices (V) which are connected by a set of edges (E). In mathematical terms, G = (V, E), but don’t let the math discourage you from delving further into this, it’s really quite intuitive! We can visualize a graph as a set of points (called vertices) with lines (called edges) connecting related vertices. A simple graph modeling a subway network would include stations as vertices and the lines connecting them as edges. An edge is present between two vertices if and only if there is a direct subway connection between the two stations.
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Simplex Algorithm by James Kupetz, Daphne Skipper, Karl Levy, Earl Lee, Joyati Debnath, Filippo Posta, and Violeta Vasilevska. Many STEM majors would be well-suited to study optimization or, more broadly, industrial engineering, in graduate school, but most are not aware of this exciting, in-demand field. We envision Linear Algebra students as the primary target for this module because students who are studying Linear Algebra are also a likely audience for optimization, and because Linear Algebra is so fundamental to the study of optimization. We seek to introduce the field of optimization to these students through a series of engaging activities that both reinforce concepts from Linear Algebra and develop a conceptual framework that will be useful for students who go on to study optimization in a later course.
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Spam versus Ham: An Introduction to Machine Learning through Introductory Statistics by Mariah Birgen, Wartburg College, Melanie Brown, Champlain College, and Joyati Debnath, Winona State University. Our email inboxes are bombarded daily. Most email clients now have filters to help determine whether an email received is one that was wanted to receive (“ham”) or one that was sent automatically (“spam”). However, these filters are not perfect, so sometimes a spam email lands in the main inbox, while an important email might be sent to a spam folder. In this module, students will explore different types of regression to predict whether an email is “spam” or “ham,” and then use these same skills to build a model to classify another data set. We present a second data set example that relates to climate change. This regression model is a type of artificial intelligence, and these classifying filters help protect personal security by identifying emails that may contain malicious links or phishing attempts.
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Transportation Network Risk and Disruption by Kevin Shirley, Appalachian State University and Dimplekumar Chalishajar, Virginia Military Institute. This module introduces the reader to the vulnerability analysis of a transportation system by modeling it using graphs or networks. Basic definitions and concepts from graph theory are introduced. Measures of node centrality are defined and illustrated. Using these concepts, hypothetical transportation systems are modeled and analyzed for their vulnerability to a failure or an attack. In the event of the disruption of a station or pathway, the consequences are quantified in terms of an increase in travel time or a reduction in the network capacity.
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Neighborhood Walk by Donna Beers, Clifton Morrow, and Yachi Wanyan. The goal of this module is to introduce undergraduates across a variety of disciplines to the vital field of risk assessment. Drawing upon students’ life experiences, this module challenges students to identify soft targets in their neighborhood, identify their vulnerabilities, and consider how to protect them from natural and manmade threats. Acting as citizen scientists, students will gather foot traffic data for a facility in their neighborhood. They will analyze and display the foot traffic data they gather in order to determine the time of day and day of the week when their facility has highest occupancy (i.e., greatest vulnerability), and determine an evacuation plan. Overall this module will equip students to advance the safety and security of their communities.
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Investigating Risk in Our Environment by Laurel Clifford, Laura Watkins, and Jonathan Weisbrod. FEMA’s National Risk Index interactive map provides an intriguing opportunity for exploration of areas of risk in the United States. Its appealing visual interface enables investigation of patterns in variables leading to risk, and in turn leads to examination of the relationships between these variables and how they relate to the investigator personally. This module aims to leverage the quickly-accessed visual representation of information and the readily-available data to engage entry level quantitative reasoning students in pattern recognition and critical thinking, analysis of interaction of variables related to natural hazard risk, while considering how the information impacts them personally. Ultimately we hope to spark student curiosity and encourage further self-driven investigation.
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Transportation Network Risk and Disruption by Kevin Shirley, and Dimplekumar Chalishajar. This module introduces the reader to the vulnerability analysis of a transportation system by modeling it using graphs or networks. Basic definitions and concepts from graph theory are introduced. Measures of node centrality are defined and illustrated. Using these concepts, hypothetical transportation systems are modeled and analyzed for their vulnerability to a failure or an attack. In the event of the disruption of a station or pathway, the consequences are quantified in terms of an increase in travel time or a reduction in the network capacity.
Download full module


An Introduction to Centrality Measures with a Transportation Network Defense Exercise  by James Kupetz, Jr., and Karl Levy, Ph.D. This module is intended for use in an introductory discrete math course. It should take between 1 and 2 hours to complete (perhaps with some of the questions finished up as part of a homework assignment).
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Block Maximum Techniques and River Flooding by Brian Birgen, Melanie Brown, and Joyati Debnath. The mathematical concept of Risk Assessment is fundamental in our everyday life. Almost every situation can be observed as some amount of risk involved. With an increase in extreme weather events, there is also an increased risk of property damage and loss of life.  This module introduces students to Block Maximum Techniques in Statistics through the lens of river flooding.  This particular application also reinforces the idea of a 100-year flood and 500-year flood to help students better understand the intersection of risk and probability in their own communities.  Specific class activities are suggested to be performed that will help students become familiar with the risk assessment involved with river flooding.  We also include instructions for doing the data analysis in Excel, R, and Minitab.
Download full module


Big Blow Can Blow the Budget – A data literacy activity on costs associated with natural hazards by Diana Cheng, and Angelyn Flowers. This module is a mathematical and quantitative literacy activity on interpreting data. This activity examines information from the Federal Emergency Management Agency’s National Risk Index tool, the National Oceanic and Atmospheric Administration’s Historical Hurricane Tracks tool, and the Natural Hazards Research and Applications Information Center’s Children and Disasters Special Collection of research.
Download full module


String Art: Creating, Constructing, and Computing by Melanie Brown, Catherine Buell, and Alison Marr. This module includes an introduction to String Art and the work of Mary Everest Boole and then an application of optimization using string art in differential calculus. It is designed to be flexible in its deployment in the classroom. The first part requires no prerequisite knowledge and is an ideal opportunity to highlight marginalized mathematicians while engaging a general education audience in finding patterns related to modular arithmetic. It is formatted as a guided worksheet for student use. The second part is a research question using Desmos and is formatted to guide the instructor supervising the research project. The module in its entirety is appropriate for a Calculus I audience to demonstrate another use for tangent lines that also provides an example of optimization beyond the traditional single-variable calculus optimization problems.
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Rank, Predict, Program, Play, Repeat: An Introduction to Linear Programming by Melanie Brown, Catherine Buell, and Alison Marr. This module is aimed to provide students beginning Linear Algebra an opportunity to play with advanced ideas of optimization and linear programming, typically reserved for a course in Optimization or Operations Research. The module builds upon introductory Linear Algebra ideas and implements code in MATLAB (using the Symbolic and Optimization Toolboxes) to allow students the ability to explore complex systems through framed examples and research questions. No previous knowledge of MATLAB or programming is required. It can be used as a classroom exercise or an out-of-class project.
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Legos Optimization by James Kupetz, Rusty Lee, Karl Levy, Daphne Skipper. Many STEM majors would be well-suited to study optimization or, more broadly, industrial engineering, in graduate school, but most are not aware of this exciting, in-demand field. We envision Linear Algebra students as the primary target for this module because students who are studying Linear Algebra are also a likely audience for optimization, and because Linear Algebra is so fundamental to the study of optimization. We seek to introduce the field of optimization to these students through an engaging activity that both reinforces concepts from Linear Algebra and develops a conceptual framework that will be useful for students who go on to study optimization in a later course.

After a fun, hands-on activity, we introduce optimization modeling and a simple (albeit computationally expensive) algorithm for solving linear optimization models, commonly called Linear Programs or LPs, with bounded feasible regions. The algorithm relies on the basic geometry of systems of linear inequalities. The ideas in this simple algorithm are fundamental to the Simplex Method, the linear optimization algorithm that is implemented in commercial optimization solvers and that is typically taught in a first course in optimization.
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Data Analytics Using Linear Programming and the AMPL Algebraic Modelling Language by Monika Keindl, Yu-Ju Kuo, and Nándor Sieben. Data analytics explores information contained in data sets. We start from building simple linear integer programming models, then use nonlinear and linear programming to find the optimal parameter values by minimizing the error between the observed data and the predicted results. We utilize the algebraic modeling language AMPL to build our models and then call a solver like CPLEX to find optimizers to our models. The module will introduce students to AMPL and optimization through several examples of increasing complexity.
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Simplex Algorithm

Simplex Introduction to Optimization from Theory to Data – Option 1 by Joyati Debnath, Filippo Posta, and Violeta Vasilevska. The mathematical concept of optimization is the one that is used in everyday life constantly. Almost every situation (financial, economic, business, etc.) can be modeled as an optimization problem. The mathematical field of optimization offers various principles and methods for solving quantitative problems that require maximizing or minimizing a quantity. This module aims to introduce some of these methods and algorithms (such as the linear optimization and the simplex algorithm) to an undergraduate audience. Specific class activities are suggested to be performed that will help students become familiar with the optimization in real-life problems.
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The Simplex Algorithm – Option 2 by James Kupetz, Earl Lee, Karl Levy, and Daphne Skipper.
Note: This module will have a professional development module available on Canvas by the end of 2023.
The simplex algorithm is a common method of solving linear programs. This module will define common terms used for linear programs; provide a step by step explanation of the method; provide a two dimensional graphical interpretation of the method; and demonstrate a free software tool that replicates the simplex method.
In linear algebra, the goal is to find the point that satisfies a set of equations. In linear programs, we have a set of equations that form a feasible space. Any point within that space satisfies those constraints. Another function is introduced where the goal is to maximize or minimize its value. This function is called the objective. So, the goal is to find the best solution from that set of solutions in the feasible space.
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