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Learning Math Home
Data Session 10, Grades K-2: Part B
 
Session 10 Session 10 K-2 Part A Part B Part C Part D Homework
 
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Session 10 Materials:
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Session 10, Part B:
Developing Statistical Reasoning (40 minutes)

The National Council of Teachers of Mathematics (NCTM, 2000) identifies data analysis and probability as a strand in its Principles and Standards for School Mathematics. In grades pre-K through 12, instructional programs should enable all students to do the following:

 

Formulate questions that can be addressed with data, and collect, organize, and display relevant data to answer them

 

Select and use appropriate statistical methods to analyze data

 

Develop and evaluate inferences and predictions that are based on data

 

Understand and apply basic concepts of probability

In pre-K through grade 2 classrooms, students are expected to do the following:

 

Pose questions and gather data about themselves and their surroundings

 

Sort and classify objects according to their attributes, and organize data about the objects

 

Represent data using concrete objects, pictures, and graphs

 

Describe parts of the data and the set of data as a whole to determine what the data show

Children at the K-2 grade level readily notice individual data points and are able to describe parts of the data -- where their own data fall on the graph, which value occurs most frequently, and which values are the largest and the smallest. A significant development in children's understanding of data occurs as they begin to consider the set of data as a whole. To focus students' attention on the shape and distribution of the data, it is helpful to build from children's informal language to describe where most of the data are, where there are no data, and where there are isolated pieces of data. The words clusters, clumps, bumps, and hills highlight concentrations of data. The words gaps and holes emphasize places in the distribution that have no data. The phrases spread out and bunched together underscore the overall distribution. Teachers must continually emphasize and help students see that what they notice about the shape and distribution of the data implies something about the real-world phenomena being studied.

The line plot (also commonly referred to as a dot plot in elementary classrooms) below displays the raisin data collected by the students in Ms. Sabanosh's first-grade classroom:
Note 3

Problem B1

Solution  

Imagine yourself in a conversation with the children about this data. A key question you might ask the students is, "What do you notice about the data?" Using informal language (clusters, clumps, bumps, hills, gaps, holes, spread out, or bunched together), write five statements that you hope children would make describing the set of data as a whole.

Too often, children describe data as numbers devoid of context. Another key question you should frequently ask students regarding their observations is, "What does that tell us about the number of raisins in a box?"


 

Problem B2

Solution  

For each of the five statements you wrote in Problem B1, indicate what that observation might imply about the real-world context of the number of raisins in a box.


 

Problem B3

Solution  

What are some questions about the data that you would pose to the students in Ms. Sabanosh's first-grade classroom to encourage them to further consider the statistical ideas of outliers, variation, center of a data set, and sampling?

Join the discussion! Post your answer to Problem B3 on Channel Talk, then read and respond to answers posted by others.


 
 

Comparing data sets prompts students to look at and describe a data set as a whole in order to see how the characteristics of one group compare to the characteristics of another. Now we'll examine data from another class not featured in the video. The line plot below displays the raisin data collected by Mr. Mitchell's second-grade classroom: Note 4


 

Problem B4

Solution  

Imagine that Mr. Mitchell's class and Ms. Sabanosh's class compared each other's raisin data to their own data.

a. 

What are four or five statements you would anticipate children might make as they compare the two data sets?

b. 

Which of these statements compare individual pieces or parts of the data, and which statements compare the data sets as a whole?


 
 

Data investigations begin by asking a question that can be answered by gathering data. It's important to formulate a question with an understanding of the type of data you'll need to collect. In this course, you studied two types of data, quantitative and qualitative. Quantitative data, such as the number of hours of television watched each week, are often referred to as numerical data; qualitative data, such as your favorite flavor of ice cream, are referred to as categorical data. The following statistical questions were gathered from pre-K through grade 2 classrooms:

a. 

How many raisins are in a box?

b. 

How far can you jump?

c. 

Who is your favorite author?

d. 

How did you get to school today?

e. 

Are you 6 years old?

f. 

How many people are in your family?

g. 

Will you go on the field trip to the zoo?

h. 

How many pockets do you have today?

i. 

How tall are you?

j. 

Do you like chocolate milk or white milk better?

k. 

What is your favorite restaurant?

l. 

Which apple do you like best: red, green, or yellow?


 

Problem B5

Solution  

For each question above, identify the type of data that will be collected and an appropriate way to display the data (e.g., line plot, bar graph).


 

Problem B6

Solution  

Formulate 10 questions that would be appropriate for your grade level and of interest to your students, five of which involve collecting qualitative (categorical) data, and five of which involve collecting quantitative (numerical) data.


 

Principles and Standards for School Mathematics (Reston, VA: National Council of Teachers of Mathematics, 2000). Standards on Data Analysis and Probability: Grades K-2, 108-115.
Reproduced with permission from the publisher. Copyright © 2000 by the National Council of Teachers of Mathematics. All rights reserved.

Next > Part C: Inferences and Predictions

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