Leveraging Cost-Effective Robotics for K-12 STEM Education through Water Quality Monitoring Tasks
Rishi Mukherjee, Andrew Ruiz, Travis Henderson, Resha Tejpaul, Kris Simonson, David Mulla, Brian McNeill, Nikos Papanikolopoulos, Junaed Sattar
AI summary
Problem
Traditional water quality monitoring is costly and inaccessible, while K-12 STEM education often fails to connect abstract concepts to tangible real-world impact. This paper addresses the gap in affordable, open-source robotics platforms that can bridge environmental science and hands-on engineering for citizen science.
Approach
The authors designed the Jar Jar ROV, a sub-$50 open-source underwater robot with a modular sensor pod, and deployed it in a large-scale educational program where middle school students built, programmed, and operated the ROVs to monitor local lakes.
Key results
- Developed a validated, low-cost open-source ROV platform with integrated water quality sensors
- Generated nearly 11,000 validated water quality measurements from over 100 student teams
- Identified a sharp engagement drop during complex electronics assembly and data uploading
- Established a scalable educational framework for K-12 citizen science and robotics integration
Why it matters
It provides educators and citizen science organizers with a scalable, affordable model to inspire middle school students in STEM while simultaneously contributing valuable environmental data to grassroots monitoring networks.
Abstract
Engaging K-12 students in authentic scientific research remains a significant challenge, particularly at the intersection of environmental science and robotics. We in- troduce the Jar Jar ROV, a low-cost, open-source Remotely Operated Vehicle (ROV) platform designed for citizen science- based water quality monitoring by middle school students. This paper presents the design of the platform and the results of a large-scale deployment with over 100 students across a US state who built, programmed, and deployed the ROVs in local lakes. The educational framework yielded high student engagement in hands-on activities, with ROV construction earning a perfect av- erage score from mentors. From a scientific standpoint, the pro- gram successfully established a grassroots monitoring network, generating nearly eleven thousand validated measurements of temperature, pH, dissolved oxygen, and turbidity. However, our evaluation identified a critical “engagement gap,” with student interest declining sharply during more complex tasks such as electronics assembly and data uploading. This paper contributes both a validated, scalable model for integrating robotics into environmental education and a clear, data-driven roadmap for future improvements. These enhancements focus on lowering technical barriers and creating a more intuitive link between data collection and scientific discovery, addressing a key challenge in empowering the next generation of citizen scientists.