Lesson 2: Finding Trends with Visualizations

External Tool | Presentation

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.

Note: 12/2018 - there have been reports of classrooms encountering a 429 error on the Google Trends tool, which results in students being blocked from using the tool for a day or so. It appears that Google Trends is interpreting all of the student requests as originating from a single IP address. Below is a recommendation for running the classroom in a manner that should avoid this issue.

Purpose

The two main purposes of this lesson are:

  1. Navigating and using a real data tool (Google Trends, see below) that is external to the course
  2. 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

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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

Links

Heads Up! Please make a copy of any documents you plan to share with students.

For the Students

  • Check Your Understanding
  • 2
  • 3
  • (click tabs to see student view)
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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.


Click to Enlarge

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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.


Click to Enlarge

Standards Alignment

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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.