This lesson is part of an Idea Set called Tracking Our Plastic: Sea to Source.

Preparation

Recommended Prior Activities:

• The Power of Plastic
• Sea to Source
• Where The Rivers Run
• Watching Water
• Why People Matter

Resources Provided:

• Quantitative Data (Encyclopedic Entry)
• Qualitative Data (Encyclopedic Entry)
• Fast Facts About Plastic Pollution (Infographics)

Required Technology:

• Internet access
• One computer per pair
• Projector
• Color printer

Physical Space

• Classroom

Setup (optional):

• For Step 2: Use copies of the infographics in Fast Facts About Plastic Pollution to create a gallery walk.

Grouping:

• Large-group learning
• Small-group learning
• Small-group work

Overview

Scientists use a variety of data collection strategies to study plastics in watersheds, and each yields different information about the environment.

• Geospatial data tracks locations—for example, the latitude and longitude of a piece of plastic pollution.
• Observational data can encompass many types of information gleaned through simple recognition (such as the number of plastic bottles at a data collection site), or measured with tools (such as the weight of plastic pollution on a scale).
• Finally, social science data focuses on people, culture, and society—for example, interviews with residents and local businesses might reveal barriers to plastic recycling.

All types of data belong to one of two major categories: quantitative and qualitative. Quantitative data come in the form of numbers, used to answer questions such as “how many?” or “how often?” In contrast, qualitative data are expressed as categories or descriptions. Both types of data are important and can be complementary; for example, one might collect qualitative data on the categories of plastic objects in a watershed, and quantitative data on the number of plastic objects in each category.

Objectives

Students will:

• Accurately read and create graphical representations of data.
• Differentiate between quantitative and qualitative data.
• Describe the value of data visualization.

Teaching Methods:
Reading: Occurs when a learner reads something, whether independent, guided, or in pairs, small groups, or with the whole class.

Modeling: During modeling, a teacher provides students with an explicit model of a skill or concept, including describing the skill or concept and its features, breaking it into understandable chunks, modeling its use or application, and periodically checking students’ understanding before asking students to demonstrate the use of the skill or concept. 

Cooperative Learning: Learning situation in which students work together in small groups and receive rewards or recognition based on their group's performance.

Skills Summary

This activity targets the following skills:

• Science and Engineering Practices
  • Analyzing and interpreting data
• 21st Century Student Outcomes
  • Learning and Innovation Skills: communication and collaboration
  • Information, Media, and Technology Skills: information, communications, and technology (ICT) literacy
  • Life and Career Skills: productivity and accountability
• 21st Century Themes
  • Environmental Literacy
• Critical Thinking Skills
  • Analyzing
  • Evaluating
  • Creating

Directions

1. Prompt students to read and discuss to learn about qualitative and quantitative data.

• Remind students that multiple types of data exist, and ask them to recall the three main data collection types from the “Sea to Source: Ganges” expedition:
  • Geospatial
  • Observational
  • Social science
• Explain that there are, in fact, many ways to categorize data, and that the class will define two important categories today: qualitative and quantitative. Use the roots qual- (type of quality, characteristic, nature) and quant- (measurable) to help students predict the meaning of these new terms (see Background Information).
  • Prompting students to define the words quality and quantity may help with this.
• Assign the Quantitative Data encyclopedic entry to half of the class and the Qualitative Data encyclopedic entry to the other half. Ask students to read and annotate, either individually or in pairs, and preview the following questions before they begin:
  • What is the definition of your data category, in your own words?
  • What is one example of this data type mentioned in the article?
  • What is one example of this data type that is not mentioned in the article?
• Bring the class back together and solicit responses to these questions, writing in a visible location and encouraging students to do the same in their notes. Return to the class’s original predicted meanings, and assess whether or how they were correct.
• Reinforce that both types of data have value by revisiting the data types from the “Sea to Source: Ganges” expedition.

2. Emphasize the importance of visual data presentation with a gallery walk.

• Create a gallery walk or display for students the infographics in the Fast Facts About Plastic Pollution, which depict quantitative data related to plastic. Ask students to answer the following questions as they examine the infographics:
  • Which of these pieces of data is most surprising to you?
  • Which of these pieces of data makes you most worried?
  • Which of these pieces of data is something you expected already?
• Next, solicit responses. Ask students to reflect on how the graphical representations of data change how they experience these facts:
  • Why did the authors choose to add graphs as well as text to these facts?
  • How do the graphs help you to understand the data?
• Inform students that they will now gain practice in creating their own graphs to represent the data they have collected and will continue to collect for the unit project: to collect data on local plastic pollution and use this data to create an action plan for minimizing plastic’s effects on local waterways.

3. Model the creation of graphical representations of quantitative data from the classroom plastic waste audit.

• Return to the classroom waste audit chart, collected in The Power of Plastic activity, and prompt students to identify which of the data in this chart are qualitative, and which are quantitative. (Types of waste are qualitative, but the amounts of these types are quantitative.)
• Using a digital data analysis program, model for students how to create a simple bar graph from the charted data. With students working in small groups, use an “I Do, We Do, You Do” format for each of the following skills:
  • Enter and organize data, with columns and rows labeled to match the original classroom waste audit chart.
  • Choose and create a simple bar graph.
  • Label this chart’s title and axes.

4. Assign students to create graphical representations of data from data collection practice activities.

• Announce data collection groups created from the rankings students provided in the Why People Matter activity, and support students as they access their practice data collected in the Watching Water or Why People Matter activities.
• Assign each data collection group to categorize their practice data as qualitative or quantitative, referring back to the definitions generated during Step 1. If all of their data is qualitative, encourage students to identify how this could be quantified, for example, by coding the answers to interview questions.
  • For example, students might need to count the number of respondents replying “Yes” and those replying “No” to the question, “Does your household recycle plastic?”
• Provide each data collection group with large paper and markers to create a chart to organize the quantitative data that they collected:
  • Geospatial data may be charted by listing types and numbers of items logged.
  • Observational data may be charted by listing types and numbers of items observed or measured.
  • Social science data may be charted by listing numbers of people responding in a particular way to particular interview questions.
• When groups have charted 3-5 pieces of data, assign them to create a digital bar chart of the data, following the model provided in Step 3 and showing the ability to independently:
  • Enter and organize data, with columns and rows labeled.
  • Choose and create a simple bar graph.
  • Label this chart’s title and axes, as appropriate.

At the conclusion of the class, require student groups to digitally share their bar graphs. If time allows, project an example, asking all students to write a single sentence describing in words what they see represented graphically (as in the Fast Facts About Plastic Pollution infographics they viewed earlier).

Informal Assessment

Informally assess students’ ability to read graphical representations of data and to appropriately label their own such that information presented is accessible to an uninformed audience.

Tips & Modifications

• Tip: Step 3: If student technological literacy differs widely, consider breaking up data collection groups, and instead grouping students according to comfort level with data analysis programs.

Connections to National Standards, Principles, and Practices

Next Generation Science Standards (NGSS), available through the National Science Teacher's Association:

• Performance Expectation:
  • MS-ESS3-3. Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.
• Science & Engineering Practices:
  • Analyzing and interpreting data: Represent data in tables and/or various graphical displays (bar graphs, pictographs, and/or pie charts) to reveal patterns that indicate relationships.
• Crosscutting Concepts:
  • Patterns: Graphs, charts, and images can be used to identify patterns in data.

Common Core State Standards:

CCSS.ELA-LITERACY.RST.6-8.4 Determine the meaning of symbols, key terms, and other domain-specific words and phrases as they are used in a specific scientific or technical context relevant to grades 6-8 texts and topics.