"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it's going to a hugely important skills in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids." - Hal Varian, Chief Economist at Google

Tuva Teacher's Guide Overview

In today's world, all of us need to be able to reason with data. Analyzing data, spotting patterns, and extracting useful information from data have become gateway skills to future STEM jobs, to full participation in the workforce, and civic engagement in the 21st century.

For these reasons, the National Council of Teachers of Mathematics, the National Science Education Standards, the National Standards for History all have emphasized the importance of students learning how to evaluate arguments using available evidence, including evidence from data.

With Tuva, we are on a mission to empower your students with the skills and tools to address tomorrow's environmental, economic, and societal challenges through data.

What does this guide include?

The purpose of this guide is to empower you, the teacher, to bring critical data literacy skills into your classroom. Whether you are teaching science, math, social studies, english or any other subject, you can enable your students to visualize, analyze, and interpret real data around your curriculum topics. This guide provides a framework that you can use to create your own activities, tasks, lessons, and projects that drive data exploration, critical thinking, and quantitative reasoning for your students.

The framework below outines a graded learning progression of learning from data across different knowledge domains. It begins with the elementary level data exploration and carries through to more sophisticated reasoning with data relevant for high school students and teachers. The broad objectives and the major concepts covered are highlighted at the outset of each section. The sections are further broken-down into a series of bite-sized modules, complete with specific learning performances and example activities that illustrate the beginning and end of the progression in a given module.

How should you use this guide?

This guide is a living, working document. It is meant to be flexibile, and we fully encourage you to adapt the modules and examples provided to best fit the needs of your students, your access to technology, and your learning environment.

Section 1: Understanding Data & Data Exploration

Section Objectives: Students learn to ask and investigate data-based questions using visualizations tools and they learn how to write high-quality conclusions.

Section Layout: This section is broken up into 4 modules. These four modules are:

  • Module 1: Introductory Data Exploration
  • Module 2: Understanding Distributions
  • Module 3: Basic Data Analysis
  • Module 4: Communicating with Data

Section Content: In this section, students are introduced to the notions of data exploration and preliminary data analysis, that is, using data to answer a question. They learn to use dot plots to compare groups and distributions. This cinludes looking at shape, finding center clumps, and making numeric comparisons. The culimnating feature of this sectino is that students use strong evidence from the data to communicate their findings and conclusions.

Module 1: Introductory Data Exploration

About the Module: The module kick-starts with an exploration of the context of the data, that is, the various attributes defining the data. This is followed by actual data collection, creating a rudimentary display, posing questions and planning studies around the collected data. The module is capped off with students digging deeper into attributes and their nature and categorizing them as categorical or numerical.

In this module, students will:

  • 1. Explore the context of data
  • 2. Process the raw data table
  • 3. Pose questions around data
  • 4. Plan investigations
  • 5. Define the term attribute or variable
  • 6. Classify attributes as numerical or categorical
  • 7. Identify minimum and maximum values for an attribute
  • 8. Select attributes to answer specific questions

Sample Activity 1: Introducing Attributes

In this activity, students study the attributes of different breed of dogs and compare 2 dogs using the attributes.

Sample Activity 2: Investigating Data

In this activity, students collect and display data and plan an investigation.

Sample Activity 3: Exploring Graphs and Attributes

In this activity, students study multiple attributes and classify them as categorical or numerical.

Notes: None of the activities above require graphing skills.

Module 2: Understanding Distributions

About the Module: The key feature of tihs module is data visualization using dot plots. Visualizing and analyzing multiple attributes helps students get a fuller picture of the data at hand. In the last activity of the module, students are encouraged to use informal terms such as clump, bumps, gap or draw parallels with common objects such as a hat or a tumbler - to describe the visual summary. This is the beginning of describing the central tendency and distribution of the data which is a crucial step in data analysis.

In this module, students will:

  • 1. Display 2 attributes simultaneously
  • 2. Describe shapes of distributions informally

Sample Activity 1: Data Visualization

In this activity, students display and analyze two attributes at the same time.

Sample Activity 2: Describing Distributions

In this activity, students describe distributions of attributes using dot plots.

Notes: Activities in this module require basic graphing skills.

Module 3: Basic Data Analysis

About the Module: The module on data analysis is about looking for patterns, discovering central trends, and reasoning with data. It marks a shift from thinking about individual cases to studying groups. Students get a chance to find typical values and trends for groups qualitatively and then use informal notions of percentages, mean, median, and mode to summarize trends quantitatively.

In this module, students will:

  • 1. Experimenting with creating dot plots to make comparisons
  • 2. Compare one case to a group of cases
  • 3. Explain the meaning of the term typical
  • 4. Identify center clumps to find out typical values for a group
  • 5. Apply the notion of percentages to find the middle 50% of the group

Sample Activity 1: Visualization and Comparison

In this activity, students display and analyze two attributes at the same time.

Sample Activity 2: Identifying Typical Trends

In this activity, students describe groups and their typical trends.

Module 4: Comparing Groups

About the Module: This module is structured to help students build their repetoire of ways to make comparisons and address the tendency to compare data in just one way. Towards this end, the singular activity in the module encourages them to make not only qualitative comparions but also numerical ones using shapes, measures of center as well as spread. Students also get a chance to try their hands at writing conclusions to a specific question based on the comparison.

In this module, students will:

  • 1. Compare two groups using dot plots
  • 2. Use statistical measures of center and spread (mean, median, and range) to make comparions between two groups

Module 5: Communicating about Data

About the Module: Students complete the first level of the data inquiry cycle with this module. They rate their conclusion from the previous activity with the help of a rubric.

In this module, students will:

  • 1. Identify the characteristics of high-quality conclusions using a rubric
  • 2. Rewrite their own conclusions from the previous activity