Unit 3 - Data and Society ('24-'25)

In this lesson, students use the problem solving process from earlier in the course to solve a data problem. After reviewing the process, the class is presented with a decision: whether a city should build a library, pet shelter, or fire department. Students work in teams to collect information on the Internet to help them decide what should be built, then use this information to build an argument that will convince the city council of their choice. They then map what they have done to the problem solving process that they have been using throughout the course, comparing the general problem solving process to its specific application to data problems.

Question of the Day: How can we use data to solve problems in our community?

In this lesson, students go further into the collection and interpretation of data, including cleaning and visualizing data. Students first look at how presenting data in different ways can help people to understand it better, and they then create visualizations of their own data. Using the results of a preferred pizza topping survey, students must decide what to do with data that does not easily fit into the visualization scheme that they have chosen. Finally, students discuss which parts of this process can be automated by a computer and which need a human to make decisions.

Question of the Day: how can we make it easier for computers to process data?

Students begin the lesson by looking at a cake preference survey that allows respondents to specify both a cake and an icing flavor. They discuss how knowing the relationship between cake and icing preference helps them better decide which combination to recommend. They are then introduced to cross-tabulation, which allows them to graph relationships to different preferences. They use this technique to find relationships in a preference survey, then brainstorm the different types of problems that this process could help solve.

Question of the Day: How can patterns in data help us make decisions?

In this lesson, students get to practice making decisions with data based on problems designed to be familiar to middle school students. Students work in groups discussing how they would use the data presented to make a decision before the class discusses their final choices. Not all questions have "right answers" and in some cases, students can and should decide that they should collect more data. The lesson concludes with a discussion of how different people could draw different conclusions from the same data, or how collecting different data might have affected the decisions they made.

Question of the Day: how can patterns in data help make a decision?

In this lesson, students look at a simple example of how a computer could be used to complete the decision-making step of the data problem-solving process if it's given an algorithm. Students are given the task of creating an algorithm that could suggest a vacation spot. Students then create rules that a computer could use to make this decision automatically. Students share their rules and what choices their rules would make with the class data. They then use their rules on data from their classmates to test whether their rules would make the same decision that a person would. The lesson concludes with a discussion about the benefits and drawbacks of using computers to automate the data problem-solving process.

Question of the Day: How can computers help us make decisions about data?

In this lesson, students look at how data is collected and used by organizations to solve problems in the real world as well as also provide students an opportunity to reflect on the fact that in their own lives, they are intentionally and unintentionally producing data that companies collect and use. Students are presented with three scenarios that could be solved using data and brainstorm the types of data they would need and how they could collect the data. Each problem is designed to reflect a real-world service that exists. After brainstorming, students watch a video about a real-world service and record notes about what data is collected by the real-world service and how it is used. At the end of the lesson, students record whether data was provided actively by a user, was recorded passively, or collected by sensors.

Question of the Day: How is our data collected and why is it useful?

In previous lessons, students have seen how we can use data to make decisions. We've also seen that data can be collected about us constantly, leading to a larger amount of data to analyze - more than a human can handle! In this lesson, students are introduced to the concepts of Artificial Intelligence and Machine Learning using the AI for Oceans widget. First students classify objects as either "fish" or "not fish" to attempt to remove trash from the ocean. Then, students will need to expand their training data set to include other sea creatures that belong in the water. In the second part of the activity, students will choose their own labels to apply to images of randomly generated fish. This training data is used for a machine-learning model that should then be able to label new images on its own.

Question of the Day: How can machines "learn"?

To conclude this unit, students design a recommendation engine based on data that they collect and analyze from their classmates. After looking at an example of a recommendation app, students follow a project guide to complete this multi-day activity. In the first several steps, students choose what choice they want to help the user to make, what data they need to give the recommendation, create a survey, and collect information about their classmates' choices. They then interpret the data and use what they have learned to create the recommendation algorithm. Last, they use their algorithms to make recommendations to a few classmates. Students perform a peer review and make any necessary updates to their projects before preparing a presentation to the class.

Question of the Day: How can I use data to make my own recommendations?