K-2.DA.9 - Identify and describe patterns in data visualizations, such as charts or graphs, to make predictions.[Data & Analysis (concept) + Inference & Models (practice)]
Data can be used to make inferences or predictions about the world.
In the Math standards, the Measurement & Data domain addresses how students organize information and represent the data using models.
In Kindergarten, the standard K.MD.B.3 asks students to classify objects into given categories; count the numbers of objects in each category and sort the categories by count.
In first grade, the standard 1.MD.C.4 calls for students to organize, represent, and interpret data with up to three categories; ask and answer questions about the total number of data points, how many in each category, and how many more or less are in one category than in another.
In second grade, the standard 2.MD.D.10 requires students to draw a picture graph and a bar graph (with single-unit scale) to represent a data set with up to four categories. Solve simple put-together, take-apart, and compare problems using information presented in a bar graph.
In the NGSS standards, Practice 2: Developing and Using Models, students use models to represent a system, to aid in the development of questions and explanations, to generate data that can be used to make predictions, and to communicate ideas to others. Modeling in K–2 builds on prior experiences and progresses to include using and developing models (i.e., diagram, drawing, physical replica, diorama, dramatization, or storyboard) that represent concrete events or design solutions.
Furthermore, the cross-cutting concept, Patterns, asks students to notice similarities and differences leading to ideas for how phenomenon might be classified.
In standard, 2-PS1-1, students plan and conduct an investigation to describe and classify different kinds of materials by their observable properties. While in, 2-PS1-2, students analyze data obtained from testing different materials to determine which materials have the properties that are best suited for an intended purpose.
In this math-aligned example, students will take the role of a new restaurant owner who is trying to figure out which menus items will be most popular.
Slide 1 - Students can choose some food items to create a small breakfast menu and self-select which one they would choose.
Slide 2 - Students are to make a prediction (with a grade appropriate explanation.)
Slide 3 - Students get into small groups, collect the other members' favorites, and then graph the results.
Slide 4 - Students analyze the results and make a prediction.
Slide 5 - Students have the opportunity to graph the class' results (here results were collected using this padlet.)
Slide 6 - Finally, students compare and analyze the class results.
Using the results from the class graph, students can further investigate various food chain breakfast menus for patterns and infer why they offer the breakfast foods that they do.
Teacher can also collect a greater amount of data by creating a Google Form to send to families or post on social media for the students.
The following video shows how McDonald's is using machine learning to make predictions about which items to promote in their drive-thru's.
Safe link: https://video.link/w/G3rqb
For example, students could record the number of each color of candy in a small packet. Then, they compare their individual data with classmates. Students could use the collected data to predict how many of each colored candy will be in a full size bag of like candy. (CA CCSS for Mathematics K.MD.3, 1.MD.4, 2.MD.10)
Alternatively, students could sort and classify objects according to their properties and note observations. Students could then create a graph or chart of their observations and look for connections/relationships (e.g., items that are hard are usually also smooth, or items that are fluffy are usually also light in weight.) Students then look at pictures of additional objects and make predictions regarding the properties of the objects pictured. (CA NGSS: 2-PS1-1, 2-PS1-2)