Patterns & Data 1: Intro to Patterns & Data

In order to see the importance of patterns, you must make use of data and in order to make use of data, you must also understand the importance of patterns. Data can be collected on any body of work. Data collection can come in the form of anecdotal student feedback on a project, tracking student grades on a bell curve to assess the appropriate amount of rigor, or on a large scale for organizations and governments to determine the effectiveness of their initiatives.

Those who collect data will tell you that all the data in the world will mean nothing if it is not organized and presented meaningfully. Patterns (repeating similarities among objects, ideas, and problems) allow analysts, students, teachers, researchers, etc. to find and give meaning to their data so it can be presented to others.

In this exercise, you will study how patterns formulate and how to make use of them. You will explore how information is represented by computers, and look at ways to engage students with data oriented tasks in order to help them notice trends. You will also address the use of patterns in creative tasks in future exercises.

In this unit when we discuss patterns, we are talking about Pattern Recognition! Pattern Recognition is the process of analyzing data and identifying the similarities and connections among the different pieces of the larger problem. Patterns help us solve complex problems efficiently. In order to make use of a pattern one must recognize, identify and make use of a pattern. Pattern Recognition helps break down the problem and also build a construct as a path for the solution.

Due to our increasing connectedness, and the inexpensive nature of sensors and computer storage, we generate data at an almost unfathomable rate. Within this data are patterns that can be hard to detect by humans. Modern machine learning techniques can allow computers to look for patterns and learn about underlying behaviors, but this process benefits greatly from a human understanding of how patterns can be detected.

Another important part of the data representation process is being able to generate visuals that help us to digest information more easily. The art of visualizing information is something that humans excel at. Finding patterns in data is the first step to determining what we want to show in our visuals.

With computational thinking, pattern recognition occurs as people study a problem and decompose it. Computational tools allow us to explore, analyze, interpret, and synthesize massive amounts of data faster than a human ever could. We can use computational methods such as data mining (examining large databases) and data federation (combining data sources) to discover new trends, patterns, and links in our understanding and appreciation of humankind.

One way to engage in patterns is to represent and visualize data, which can help simplify the complexity of data and convey the message. Watch the clip below of a TEDtalk from 2010 that discusses data visualization. While watching, consider how the speaker is using pattern recognition and data visualization to make use of the data he is studying.

Whether or not you recognize it yet, ELA teachers are already well versed in the relationship between data and patterns. We read literature to identify interesting patterns in imagery, word choice, and so on. But data? Data might not be a word you hear often in ELA class, but that’s because we use different terminology: textual evidence, annotation, literary elements. These are all wonderful variations of how data operate in ELA.

Pattern and data practices in English Language Arts (ELA) focuses on identifying patterns in a given text, analyzing patterns to draw conclusions, and making comparisons between different texts.

When conducting literary analysis, a reader might look for patterns in the style of authors, organization of writing, or even word choice. Focusing on patterns would allow the reader to make a detailed analysis and provide insights into the text itself. Pattern matching can also be used to develop linguistic understanding and improve writing style.

Using patterns to understand the usage of parts of speech would assist with improved writing skills. Using patterns to track common writing mistakes can assist writers with proofreading and self-editing of writing.

In terms of data practices in ELA, writers collect data, analyze data, and communicate data as part of the research process. Using data visualization tools, writers can easily analyze data they have collected to draw conclusions to communicate in writing or they can use data visualization as a unique form of communication. Data practices and pattern matching can also be used by readers and authors to identify missing perspectives in the landscape of literature.

In Social Studies, we don’t only look at the past, we also look at the present and future. Studying data helps us identify the patterns between social/historical events to explain and/or make predictions on the analyzed phenomena.

Researchers, political scientists, museum curators, and sociologists are always using pattern recognition to look for patterns in their areas of expertise and rely on data practices to be systematic in their collection, analysis, and communication of that data. One might consider how common economical, political, and social issues repeat themselves across different historical civilizations.

The use of political polls in the election process is an excellent example of how patterns and data practices are used in the field of social studies.

Demographers design surveys, administer those surveys, and publish the reports. The results of these reports influence campaign spending decisions, speechwriters, and can also affect public opinion. Digital tools offer a variety of ways to publish data providing visualization that convey their own messages. Combining different visualizations can provide deeper insights into how different sources of data might be related which, in turn, can impact campaign decisions or candidates’ platforms.

There are several examples of recognizing patterns in data for problem-solving in everyday life and across the curriculum. In addition to the examples in previous steps, this article from Equip Learning (article found here) provides great context that can assist you in answering the reflection questions below.