Lesson 2: Finding Trends with Visualizations
Overview
Students use the Google Trends tool in order to visualize historical search data. They will need to identify interesting trends or patterns in their findings and will attempt to explain those trends, based on their own experience or through further research online. Afterwards, students will present their findings to ensure they are correctly identifying patterns in a visualization and are providing plausible explanations of those patterns.
Purpose
The two main purposes of this lesson are:
- Navigating and using a real data tool (Google Trends, see below) that is external to the course
- Getting acquainted with talking and writing about data. In particular we want to:
- Draw a distinction between describing what the data shows and describing why it might be that way
- In other words: describe connections and trends in data separate from drawing conclusions.
- We want students to get in the habit of separating the what from the why when it comes to talking and writing about data
As a bit of foreshadowing, the next lesson looks deeper into assumptions that people make about data that can lead to unintentional consequences and even exacerbate some of society's divisions.
Agenda
Getting Started (5 mins)
Activity (30 mins)
Wrap-up (5-20 mins)
Assessment
View on Code Studio
Objectives
Students will be able to:
- Use Google Trends to identify and explore connections and patterns within a data visualization.
- Accurately describe what a data visualization of a trend is showing.
- Provide plausible explanations of trends and patterns observed within a data visualization.
Preparation
- Use the Google Trends tool to familiarize yourself.
Links
Heads Up! Please make a copy of any documents you plan to share with students.
For the Students
- Exploring Trends - Activity Guide
- Google Trends - Link
Teaching Guide
Getting Started (5 mins)
Discussion Goal
Goal: Quickly connect today’s activity to previous day, but then move into the Google Trends activity.
Introduce: Data Stories
Remarks
Yesterday we started to look at the ways big data is changing lots of fields. Today we're going to start looking a little more closely at what we can learn from data. In particular, how can we use data to learn or "tell a story".
Activity (30 mins)
Content Corner
Search trends are used in a variety of fields in order to understand what topics are most popular across the country and world.
- Search trends are also powerful predictors.
- Medical professionals may use this information to trace an outbreak of the flu.
- Businesses, media outlets, and advertisers keep a close eye on trending topics in order to understand how potential customers are thinking.
The fact that a global "conversation" is now happening online and computational tools exist to capture and visualize that conversation enables entirely new ways of identifying, understanding, and predicting patterns in culture and society at large.
Exploring Google Trends
Distribute: Activity Guide - Exploring Trends - Activity Guide
Teaching Tip
Demonstrate the Tool: You may wish to demonstrate how to use Google Trends in front of the class before asking them to use it themselves. You could use the following steps:
- Ask students to recommend a search term to display.
- As a class, speculate as to what the trend might be showing.
- Add a second term to your visualization and discuss quickly what it might be showing.
- Demonstrate the ability to select different time periods, regions, etc.
Note: The front page of Google Trends shows a collection of stories compiled by others. To actually use the tool yourself, you need to enter text into the “Explore” trends. For more help with Google Trends, you can see the Google support page on the subject.
As a class or individually students should read the summary at the top of the activity guide, which explains what information they will be looking at and how to use the Google Trends tool.
Students will use Google Trends a tool which visualizes data taken from Google search histories all around the world from the past several years.
- Students will work individually or in pairs to identify topics they wish to examine in greater detail.
- They should spend some time just exploring the tool, but eventually they will need to choose a single topic or set of topics that they will use to answer the questions that appear on the bottom of the activity guide.
Tell a Story
Students should find a trend or set of trends they think is particularly interesting or personally relevant and try to tell a story from the data they see. Students will write down:
- A description of what they were trying to look for
- An accurate description of what the visualization is showing
- A plausible explanation of why that trend might have happened.
Wrap-up (5-20 mins)
Discussion Goal
Provide students a chance to share their findings. Ensure students are accurately describing trends in the charts and that their stories or explanations for these trends are reasonable.
Share Data Stories
Once students have developed their charts and responded to the questions, have them share their “data stories” with each other.
Each group or individual should only take a minute or so to present their chart and story, after which the class might ask questions or add their own interpretations of the chart. Good questions include:
- Is the story the students told supported by the chart?
- Are there other ways to interpret the chart?
- Are there additional terms you’d also like to see shown on the chart?
Teaching Tip
For sharing, you may want to: Bring the whole class together. Have individuals share with an elbow partner or in a small group.
It is likely that students are going to want to play with Google Trends individually, so having them share with a small group might be less intimidating. However, with small groups you will need to circulate and be vigilant about ensuring that students are emphasizing the right things and asking critical questions. You might find it easier to ensure that students see the right kinds of critical questioning as a whole class.
Remarks
It’s exciting to be able to look at so much data in such a concise way, and it certainly feels like we’ve seen a lot of good stories here. As we start thinking more about how we use data, however, we’ll need to make sure that the assumptions we’re making about our data are correct.
(Optional) Collect: student activity guides. Students may want to revise their stories after the next lesson. Hold onto their activity guides or ask them to do the same so that they can update their assumptions if necessary after the next lesson.
Assessment
Score Activity Guides
- Collect and Grade
- Have students do peer review
Code Studio: Assessment questions are available on the Code Studio.
- Lesson Overview
- Student Overview
Teaching Tip
The most accurate description is that People search for "dogs" more frequently than "cats".
All of the other responses inject some form of rationale or reason for why dogs are searched for more frequently than cats, rather than just describing what the data shows. From this graph alone we have no idea why it's showing this way.
Student Instructions
Below is an image from Google Trends that plots Cats and Dogs.
Choose the most accurate description of what this data is actually showing based on what you know about how Google Trends works.
Student Instructions
Consider the Google Trends graph of dogs and cats below (same graph as previous question).
Give a plausible explanation or hypothesis for the spike in dog searches that occurred between 2014 and 2015 that would lead to further investigation or research. Give your explanation and what you would want to investigate next.
Standards Alignment
View full course alignment
Computer Science Principles
3.1 - People use computer programs to process information to gain insight and knowledge.
3.1.1 - Use computers to process information, find patterns, and test hypotheses about digitally processed information to gain insight and knowledge. [P4]
- 3.1.1A - Computers are used in an iterative and interactive way when processing digital information to gain insight and knowledge.
- 3.1.1B - Digital information can be filtered and cleaned by using computers to process information.
- 3.1.1E - Patterns can emerge when data is transformed using computational tools.
3.1.2 - Collaborate when processing information to gain insight and knowledge. [P6]
- 3.1.2A - Collaboration is an important part of solving data driven problems.
- 3.1.2B - Collaboration facilitates solving computational problems by applying multiple perspectives, experiences, and skill sets.
- 3.1.2C - Communication between participants working on data driven problems gives rise to enhanced insights and knowledge.
- 3.1.2D - Collaboration in developing hypotheses and questions, and in testing hypotheses and answering questions, about data helps participants gain insight and knowledge.
- 3.1.2E - Collaborating face-to-face and using online collaborative tools can facilitate processing information to gain insight and knowledge.
- 3.1.2F - Investigating large data sets collaboratively can lead to insight and knowledge not obtained when working alone.
3.1.3 - Explain the insight and knowledge gained from digitally processed data by using appropriate visualizations, notations, and precise language. [P5]
- 3.1.3A - Visualization tools and software can communicate information about data.
- 3.1.3B - Tables, diagrams, and textual displays can be used in communicating insight and knowledge gained from data.
- 3.1.3C - Summaries of data analyzed computationally can be effective in communicating insight and knowledge gained from digitally represented information.
- 3.1.3E - Interactivity with data is an aspect of communicating.
3.2 - Computing facilitates exploration and the discovery of connections in information.
3.2.1 - Extract information from data to discover and explain connections, patterns, or trends. [P1]
- 3.2.1A - Large data sets provide opportunities and challenges for extracting information and knowledge.
- 3.2.1B - Large data sets provide opportunities for identifying trends, making connections in data, and solving problems.
- 3.2.1C - Computing tools facilitate the discovery of connections in information within large data sets.
- 3.2.1D - Search tools are essential for efficiently finding information.
- 3.2.1E - Information filtering systems are important tools for finding information and recognizing patterns in the information.
CSTA K-12 Computer Science Standards (2017)
DA - Data & Analysis
- 3A-DA-10 - Evaluate the tradeoffs in how data elements are organized and where data is stored.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.