Trees in a Diagnosis Game

(View Complete Item Description)

In this dynamic data science activity, students use data to build binary trees for decision-making and prediction. Prediction trees are the first steps towards linear regression, which plays an important role in machine learning for future data scientists. Students begin by manually putting “training data” through an algorithm. They can then automate the process to test their ability to predict which alien creatures are sick and which are healthy. Students can “level up” to try more difficult scenarios.

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

Stella

(View Complete Item Description)

In Stella, students act as astronomers, studying stars in a “patch” of sky in our own galaxy. Using simulated data from spectroscopy and other real-world instrumentation, students learn to determine star positions, radial velocity, proper motion, and ultimately, degree of parallax. As students establish their expertise in each area, they earn “badges” that allow them greater and easier access to the data. The Teacher Guide includes background on stellar spectroscopy (the brightness of a star), photometry (the breakdown of light from a star), and astrometry (measuring the positions of stars).

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

BARTy

(View Complete Item Description)

Modeling traffic data is important for urban planning, creating transportation systems, and even predicting how much foot traffic a retail store can expect in a given day. This genre of dynamic data science activities could be classified as “finding a needle in a haystack,” giving students a chance to mine big data to make insights about traffic use. According to the Bay Area Rapid Transit District, about 400,000 people used the BART system daily in 2018. In BARTy, students investigate BART data from 2015 to learn about passenger use and explore traffic patterns. The Teacher Guide includes a game-like investigation to locate a “mystery meeting,” and suggests ways to help students figure out peak passenger use, popular stations, and the impact of events in San Francisco on BART usage.

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

National Health and Nutrition Examination Survey (NHANES) Data Portal

(View Complete Item Description)

Access and explore large datasets from the National Health and Nutrition Examination Survey (NHANES, 2003). Working with large datasets that emphasize exploration, finding patterns, and modeling is an essential first step in becoming fluent with data. This activity is a great place for students to start, since the dataset is straightforward and students can decide on the data they want to explore, including height, age, weight, and many other health-related attributes. Students begin by selecting and then investigating subsets of the dataset, for example, to find the cholesterol level of U.S. citizens. Then, working with their classmates or individually, students can try their own data science challenges, such as finding health trends in a subset of Americans by their household income, age, or marital status, etc.

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

Geiger

(View Complete Item Description)

In this dynamic data science game, students try to track down a speck of extremely dangerous radioactive material (the "source"), which has been lost somewhere in the middle of their lab. A special device measures the strength of the radiation and, if it’s positioned correctly over the speck, can be used to collect it for safe disposal. But it's a tiny speck, so they have to give quite precise coordinates. They take measurements to figure out the speck’s location, but must beware: as they take measurements, they're also accumulating radiation exposure. If they get too much, they’ll lose the game and will have to start over. Can they find the source before it’s too late? Using mathematical models, students generate useful strategies for winning the game with data.

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

Stebbins

(View Complete Item Description)

Stebbins is a game about evolution. Students collect data as predators “eating” colored circles on a colored background, being careful to avoid the poisonous ones. Data analysis reveals how the population changes color over time, and can be used to illuminate a common misconception that individuals change in response to predation. Stebbins is modeled on a non-digital game-like simulation of natural selection created by evolutionary biologist G. Ledyard Stebbins.

Material Type: Activity/Lab, Simulation

Author: Concord Consortium

Bootstrap: Data Science Pathway

(View Complete Item Description)

In Bootstrap:Data Science, students form their own questions about the world around them, analyze data using multiple methods, and write a research paper about their findings. The module covers functions, looping and iteration, data visualization, linear regression, and more. Social studies, science, and business teachers can utilize this module to help students make inferences from data. Math teachers can use this module to introduce foundational concepts in statistics, and it is aligned to state and national standards.

Material Type: Activity/Lab, Assessment, Data Set, Full Course, Homework/Assignment, Lecture Notes, Lesson Plan, Teaching/Learning Strategy

Authors: Ben Lerner, Dorai Sitaram, Emmanuel Schanzer, Emma Youndtsmith, Flannery Denny, Jennifer Poole, Joe Politz, Nancy Pfenning, Shriram Krishnamurthi

Data Talks Archives

(View Complete Item Description)

Data talks are short 5-10 minute classroom discussions to help students develop data literacy. This pedagogical strategy is similar in structure to a number talk, but instead of numbers students are shown a data visual and asked what interests them.

Material Type: Teaching/Learning Strategy

Author: YouCubed