Mathematics-related projects
| Grade Level | Applicable Standards | Module Summary | Year | Developer | Link |
|---|---|---|---|---|---|
| 1 | Forthcoming | Forthcoming: Machine Learning, Algorithms, Binary Data | 2020 |
Michelle Sanchez Prairie Vista Elementary School |
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| 3 | Forthcoming | Forthcoming: Machine Learning | 2020 |
Amanda Fox Prairie Vista Elementary School |
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| 7 | 7.RP.3 7.NS.3 7.EE.4 |
At the end of this module, students will be able to use social media manipulation research data to calculate percent change and unit price. | 2021 |
Michael Dimino Edwardsburg Middle School |
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| 8 | Indiana: Computer Science 6-8.DI.1, 6-8.PA.2, Science and Engineering Process Standards SEPS.1, SEPS.2, SEPS.3; NGSS Standards: Engineering Design MS-ETS1-2; ITEEA Standards: 8.F, 12.J; CSTA Standards: Algorithms and Programming 2-AP-10, 2-AP-11. | In this module 8th grade students will be introduced to a real-world problem involving airline scheduling, learn a graphical way to solve the problem, test several algorithms for heuristic solutions, and learn basic coding to implement a chosen algorithm. | 2018 |
Erica Price Trinity School at Greenlawn |
Zip Archive (link) |
| 9 | Indiana: PS.1, PS.3, PS.6; ITEEA: 6, 10; CSTA 5.3.A.CT.1, 5.3.A.CL.2 | In an increasing move away from paper textbooks and toward one-to-one technology, students are beginning to struggle with connecting what they have learned in the classroom to what they are practicing at home. Notes are taken using cameras on smartphones and anything new or different is immediately labeled as too difficult by many students who are math-phobic. This module will introduce students to a summarization technique that will personalize the math they are learning and cement the knowledge into their long-term memories | 2016 |
Michael Dimino Concord High School |
Zip Archive (link) |
| 9 | Indiana: AII.SE.2, AII.SE.3; ITEEA: 1, 2. | In this module we will briefly review the Algebra 1 skills of solving equations and graphing lines. We will then look at the skills of solving a system of two equations and two unknowns both graphically and algebraically. We will then extend this concept to a system of 3 equations and 3 unknowns. Throughout the unit we will look at application problems that are all focused around the concept of a transistor. This module is set up for an Algebra II course that is set on a block schedule where we meet daily. It could be adapted to a traditional block schedule or another schedule by combing lessons and application problem sets. | 2016 |
Vincent Ferro Mishawaka High School |
Zip Archive (link) |
| 10 | Indiana: G.LP.1, G.LP.2, G.LP.3, G.T.8, 6-8.DI.3, 6-8.DI-5, 6-8-PA.1, 6-8.PA.2, 6-8.PA.3, 6-8.NC.1, 6-8-NC.2 | The facial recognition project based learning module will be used as a “hook” for developing students interest in the Geometry course that lies ahead. Students will experience real world connections between geometric points and line segment distance as it corresponds to a frontal facial image. Students will see the need to develop their skills in mathematics and computers as viable tool for one’s future | 2016 |
Allen Westendorp Clay High School |
Zip Archive (link-01) (link-02) |
| 11 | Forthcoming | This module provides an introduction to Python data frames and dictionaries -- i.e., to create data set in Python -- for high school math students. It considers how to draw plots, determine a prediction model that best fits the data, and use the model to predict something new. Students also analyze test data with Python to generate various statistical measurements, etc. | 2022 |
Md Nur Islam Culver Academies |
Zip Archive (link) |
| 11 | Indiana: PC.F.1, PC.F.2, PC.F.4, PC.F.5, PC.F.6, PC.F.7, PC.QPR.1, PC.QPR.2, PC.EL.4 | Students will be capable of Modeling Data with Functions. They will In this three-week unit, students will review and extend their knowledge of polynomial functions (Linear, quadratic, cubic) and the potential models they are capable of making with and without technology. Students will further their knowledge of the importance and the role of technology while continuing to analyze functions and their properties. Students will review and extend their knowledge of the algebra and geometry of transformations and will review the usefulness of organizing, displaying, and graphing their results by hand and with graphing utilities and computer spreadsheet software. The unit concludes by performing a data scrape from a website onto a computer spreadsheet, model the data with a polynomial function, analyze the data in order to determine correlation, make predictions, draw conclusions, or infer relationships between the data. Students will interpret, analyze and organize the data, build tables and graphs, and use graphing utilities to support conclusions made from the data. | 2016 |
George Logsdon Riley High School |
Zip Archive (link) |
| 10, 11, 12 | Forthcoming | To increase understanding of Trigonometry and demonstrate real-world applications of the skills. | 2022 |
Adolph Soens Washington High School |
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| 10, 11 | ITEEA: 13.J; 17.N; Indiana: PS.3, PS.DA.1 | Students will work in pairs to select data of their interest from a variety of Internet sites, including ESPN, IMDB, Wolfram Alpha|Pro, etc. They will use Excel to import the data, sort and organize it, and choose pairs of variables to analyze by graphing and calculating a variety of statistics. They will ultimately report their inferences and conclusions, explaining their findings with evidence from the graphs and statistics, and discuss the logical implications of their results. | 2016 |
Thomas Falcone La Lumiere High School |
Zip Archive (link) |
| 11, 12 | Forthcoming | Forthcoming | 2018 |
Clinton Jepkema Niles High School |
Zip Archive (link) |
| 11, 12 | Forthcoming | Forthcoming | 2017 |
Thomas Finke Trinity School at Greenlawn |
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| 9, 11, 12 | ITEEA: The Nature of Technology -- 1, 2; ITEEA: Technoloy and Society -- 4, 6, 7, 8, 9, 10, 11, 12, 13 | Students will be capable of Recognizing and Modeling Linearly Related Data with Functions by hand and with the aid of technology (TI 83/84 and MS Excel or Google Spreadsheet). Students will review and extend their knowledge of linearly related data, linear functions, and be introduced to how linear regression works and how it is used in machine learning. Students will further their knowledge of the importance and the role of technology while continuing to analyze linear functions and their properties. Students will review and extend their knowledge of the algebra and geometry of transformations and will review the usefulness of organizing, displaying, and graphing their results by hand and with graphing utilities. Students will conclude their understanding of the unit by performing a basic hand computation of linear regression (least squared method), complete regression analysis with technology (also understand, line of best fit, r2, r, and the correlation coefficient and their importance and significance). | 2017 |
George Logsdon Riley High School |
Zip Archive (link) |
| 6, 7, 8 | CCSS.MATH.PRACTICE.MP1-5 | The goal of this module is to introduce students to real-world and computer science applications of mathematics and to engage students in problem solving opportunities to become familiar with persevering through problem solving and to build a “toolkit” of problem solving strategies. By the end of this module, students will have an understanding of problem solving strategies and how computer science is a diverse field that anyone can participate in. | 2021 |
Emily Graham Schmucker Middle School |
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| 9, 10 | PS.1, PS.4, PS.5, PS.6, PS.8, AI.F.1, AI.L.3, AI.L.4, AI.SEI.1 | At the end of the module, the students will be able to represent a real-world problem in multiple representations and explain their findings. | 2021 |
Jennifer LeMunyon Elkhart High School |
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| 7, 8 | Forthcoming | Forthcoming | 2018 |
Erica Price Trinity School at Greenlawn |
Zip Archive (link) |
| Forthcoming | Forthcoming | Forthcoming | 2019 |
Juan Alvarez John Adams High School |
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| Forthcoming | CCSS.MATH.CONTENT.HSS.ID.B.6; CCSS.MATH.CONTENT.HSS.ID.B.6.A; CCSS.MATH.CONTENT.HSS.ID.B.6.B; CCSS.MATH.CONTENT.HSS.ID.B.6.C; CCSS.MATH.CONTENT.HSS.ID.C.7; CCSS.MATH.CONTENT.HSS.ID.C.8 | This lesson will be using data and research compiled from the ND RET program to assist in teaching the statistical concepts of linear and exponential regression. Data points regarding Moore’s law fit an exponential curve well, while data-center and ICT energy usage is a little more complicated. There is some uncertainty with both. Assumptions are made that exponential curves fit, but linear seems to be able to cover it as well. | 2019 |
Robert Babler Niles High School |
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| Forthcoming | Forthcoming | Forthcoming | 2019 |
Kerry Davis Marian High School |
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| Forthcoming | Forthcoming | Forthcoming | 2019 |
Ronald Grosz John Adams High School |
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| 5 | Forthcoming | Forthcoming | 2019 |
Shelley Lebiedzinski Praire Vista Elementary School |