Post AP

Post AP - What’s the story?

The story of this chapter is about how data can be manipulated to extract or reveal new information. Up to this point we have been focused primarily on bits and what they can be used to represent. Now we’re taking a big step back to do the inverse: we want to use tools meant for viewing, manipulating, and visualizing data in order to extract or find new information.

The lessons in this chapter often have two things going on at once. In the background, the class is daily collecting some data about themselves (the “Class Data Tracker project”) in order to accumulate data to process later on. In the interim, students are learning about and developing skills with spreadsheet and visualization tools. The goal is for students to learn a few basic skills, see lots of examples, and then apply what they know to the Tell A Data Story project at the end of the chapter.

A big part of the story here is for students to understand the computer scientist’s role in working with data, which means emphasizing how to use tools to manipulate, compute, and visualize the data. We look at things like making sure that data type choices support the way we intend to process it later (e.g. don’t collect text when you need a number). Data inevitably gets “dirty” during collection and needs to be cleaned. Computers are really useful for doing some aggregations and visualizations to look for patterns. Along the way, we need to understand how human bias can be introduced at each step so that we can accurately convey what patterns in the data are or are not telling us. These activities help build toward the enduring understanding that there are trade offs when representing information as digital data.

Our Approach to the Content

The lessons in this chapter lean heavily on external tools, especially spreadsheets. The benefit is that students will gain experience with real tools and real data for the first time. The pitfall is that, because the tools are external, they are not scaffolded or designed for learning. We have tried to provide tutorials and curated data sets to ease the burden as much as possible, but ultimately you’re operating in the real world. While confined to the world of your classroom, the Class Data Tracker project should provide some authentic examples, scenarios, and sometimes headaches related to data collection and processing in the real world.

As the teacher it’s important to keep in mind the goals of CS Principles because it can be enticing with these lessons to dig into “hardcore” data analysis techniques and statistics. While these are important, they are beyond the scope of CS Principles. Thus, we treat data analysis and statistics a bit like an electric fence: get close, but don’t touch. Students should be able to extract interesting things as the result of letting the tools do the work. We provide some large sets of curated data that came from real sources. The data is big enough that you have to apply some computation to make sense of it. We show how to use spreadsheets to do basic aggregations (such as grouping, counting, clustering) and computations (such as average, median, etc.), without turning it into a lesson on statistics and data analysis. We want to build toward the enduring understanding that computing facilitates exploration and the discovery of connections in information.

The idea behind the Class Data Tracker project is that we have found that when students work with data that they collected themselves it is easier and intrinsically motivating for students to dig in. To accumulate enough data, we collect it in increments during the time they’re building up other skills with data tools. You should connect the skills students are learning in the exercises to similar things they might do with the class tracker data for the Tell a Data Story project.