1st Grade Water Clean Up Project

First graders engaged in an in-depth project about plastic and trash polluting our lakes, rivers and oceans. Over several weeks, students followed the design thinking process to better understand the issues, learn about how organizations are helping to fix the problem. Next, first graders worked in small groups to design and prototype their own solutions to help clean up our water systems.

Learning About the Problem

The first step of the project was for first graders to collect information to better understand how and why plastic and trash was getting into our water systems. They also learned about why plastic and trash is a problem for ocean, water and land animals and humans.

Students started with research. They read several books including Rocket says Clean Ups! by Nathan Bryon and Sam the Eco Robot & the Ghost Nets by Thassanee Wanick. They also collected facts and information from What a Waste by Jess French.

First graders learned that there are about 1.8 trillion pieces of trash in our oceans! About half of that trash comes from discarded fishing nets. These nets can trap ocean animals. They also learned that ocean animals can mistake plastic bags for jellyfish, eat them and get sick.

First graders talked about all the ways that plastic and trash can get into our rivers, lakes and oceans. They can be dumped from ships, litter that humans leave on beaches and open spaces and waste from factories.

Helping Solve the Problem

The Ocean Clean Up Project

First graders learned about the Ocean CleanUp Project, an organization that is using robots and machines to help remove plastic and trash from water systems. Students explored the different ways the organization is removing plastic from the water and trying to prevent the trash from getting into the ocean.

USing Artificial Intelligence to Help

AI Powered Cameras

First graders explored how the Ocean CleanUp Project is using artificial intelligence cameras to help track where plastic is in the oceans.

ADIS AI Powered Camera

First graders learned all about the AI powered cameras that the Ocean Cleanup Project developed to help track where plastic is in the ocean.

Collecting Data

Hundreds of AI cameras were placed on ships traveling all the oceans. This allowed the Ocean CleanUp Project to collected data of where in the world plastic was in the ocean so they learned where the most important places to send ships, collect data about how much plastic was in the waterways and share that information with governments and organizations to advocate for laws to help prevent plastic for getting into the ocean.

First Graders Trained AI models

Learning How AI Learns

We discussed how an AI machine learns and what machine learning means. Students made connections to the coding they were doing in Scratch Jr, using coding language to give instructions, and how that is different from machine learning, responding to prompts and questions, interacting with a user, and learning from those interactions.

Then, using the site Machine Learning for Kids, students explored a simple way to train and test a machine. The first step was showing students how we trained a machine to determine if a picture was of a human or an animal. We showed students how we uploaded images to the train section. We talked about how we picked pictures of all different kinds of animals so the machine could learn to recognize all different animals: animals with fur or without, small and large animals. We also showed all the different images of humans. We talked about how humans all look different: older and younger, different skin tones and hair types. We wanted the machine to learn to see all different types of animals and humans. Then we tested the machine. Each student had a different picture of a human or an animal to see if it could correctly identify what the picture showed. We tracked how many times the machine was correct or incorrect. The more we tested the machine, the more accurate it became, and we talked about how the machine was learning more and more. With each class that tested, the machine got better and better at identifying the picture correctly. We also talked about how we only trained the machine on a small number of images and that the more images we provide the machine, the better the machine should be at learning and correctly identifying the images.

Building Their Own Training Model

Now it was time for the students to become the teachers! Drawing on what they learned about the AI cameras used by the Ocean Cleanup project, we challenged the students to train an AI model to correctly identify images of ocean animals versus trash.

The first step was gathering images to "teach" the machine. We discussed how providing better, more accurate images creates a more successful learning model. If we trained the machine using inaccurate images or poor information, it wouldn't learn what it needed to know. To gather data, students explored the school building to take photos of trash in different spaces- especially after lunchtime! They focused on empty water bottles, drink containers, takeaway boxes, and snack wrappers.

Afterward, we sorted through the photos to find the highest-quality images for training. The students uploaded over 500 images of trash and approximately 150 images of ocean animals. Then, it was time to put their model to the test! Each student showed the machine a picture of an animal and a picture of trash to see if it could identify them correctly. The machine identified the trash perfectly every time, but it struggled with the ocean animals about ten times.

Reflecting on the results, the students determined that if they had uploaded more images of ocean animals- perhaps matching the 500 images of trash- the machine would have been better at identifying them. They also realized how vital it is to train a machine with high-quality, accurate information. Just as a teacher ensures students receive correct information to learn, the same is true for AI. If a machine is given poor-quality data, it cannot work "smart" and will produce inaccurate results.

This project provided a hands-on, interactive experience that helped students build a foundational understanding of artificial intelligence and machine learning. Most importantly, it showed them how technology like AI can be used to solve real-world problems like ocean pollution.

1st Graders Design to Solve the Problem

Collaboration

To prepare for their group work, students explored what it truly means to be a teammate. We began by reflecting on our own experiences, discussing what makes working with a partner both successful and positive. To deepen our understanding, we read several stories that highlight the ups and downs of teamwork:

What About Moose by Corey Rosen Schwartz and Rebecca J. Gomez
The Magnificent Idea by Ashley Spires
Boxitects by Kim Smith

Inspired by these books, each class brainstormed a list of essential qualities for a great collaborator. These Collaboration Guidelines became the gold standard for every student as they moved into their small groups to begin building their prototypes.

Problem

After learning about the impact of pollution on aquatic life, our first-grade groups were tasked with a 'Design Challenge.' They brainstormed, sketched, and built original prototypes aimed at solving a critical problem: the buildup of trash in our water systems. This project challenged them to think critically about how human innovation can help restore the health of our oceans, rivers, and lakes.

Prototyping

The project culminated in our TIDES Garage makerspace, where students brought their ideas to life! Working in teams, our young engineers first drafted detailed blueprints to map out their visions. Then, they put their collaborative skills to the test, building prototypes using a mix of cardboard, duct tape, and littleBits electronic circuits. By combining these recycled materials with modern technology, each group designed a unique, functional model aimed at tackling water pollution head-on. Below, there are links to videos and pictures of each group explaining their prototype and how they work!