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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 McNeil, Nikolaos Papanikolopoulos, Junaed Sattar

arXiv:2610.04565v1cs.ROcs.CY

TL;DR

Freshwater monitoring can be costly, and K–12 STEM activities can lack tangible real-world connections. The paper presents a low-cost, open-source ROV program in which middle-school teams build, program, and deploy vehicles for lake monitoring. The pilot produced 10,600 validated measurements, alongside high engagement in construction and data collection but lower engagement in technically complex tasks.

  • Problem

    Traditional freshwater monitoring can be costly, while STEM education can struggle to connect abstract concepts to real-world impact.

    Source: §1 · pp. 1–2

  • Method

    The authors designed a low-cost, open-source ROV and curriculum for middle-school teams to build and deploy in citizen-science water monitoring.

    Source: §1 · p. 2, §5 · p. 8

  • Results

    10,600 validated water-quality measurements were collected; student engagement averaged 5.0 for ROV construction and 1.8 for data upload.

    Source: Overall Feedback & Future Participation · p. 11, §8 · p. 14

  • Takeaways & Limitations

    The pilot demonstrates a grassroots monitoring network in which students contribute validated freshwater measurements through an educational robotics program.

    Source: §1 · p. 2, §8 · p. 14

    Some volunteer water-monitoring programs have limited measurement scope and precision, including absent pH and dissolved-oxygen readings.

    Source: §2.3 · p. 4

Abstract

from arXiv · show

Engaging K-12 students in authentic scientific research remains a significant challenge, particularly at the intersection of environmental science and robotics. We introduce 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 average score from mentors. Scientifically, the program 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 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.

1 Introduction

The paper frames freshwater monitoring and K–12 STEM engagement as linked challenges, then presents the Jar Jar ROV as a hands-on citizen-science platform. Its deployment is intended to pair robotics learning with authentic environmental data collection.

1 Introduction

  • Traditional water-quality monitoring can be costly and resource-intensive, while students often lack tangible connections between STEM concepts and real-world impact.Freshwater measures also inform human use of lakes and indicators of aquatic ecosystem health.Source: §1 · pp. 1–2
  • Students building and deploying ROVs contribute to authentic scientific work by measuring the environmental health of their own communities.The authors describe the robot as a vehicle for learning engineering, programming, and design through scientific inquiry and environmental stewardship.Source: §1 · p. 2
  • The Jar Jar ROV is a low-cost, open-source underwater robotics platform designed to support both education and citizen-science monitoring.The paper reports a deployment with over 100 students across a US state.Source: §1 · p. 2

2 Related Work

Prior work spans educational robotics and citizen-science monitoring, but the paper identifies a gap: accessible underwater platforms that combine participant engineering with useful water-quality measurements.

2.1 Educational Robotics

  • Educational robots support computational thinking and STEM learning, but high cost, competition focus, and steep learning curves can limit access.Reviews report positive learning impacts that vary with instructional methods and robot embodiment.Source: §2.1 · p. 3

2.2 Citizen Science Initiatives

  • Citizen-science projects can extend environmental monitoring, yet many use pre-built platforms or simple tools that offer little participant involvement in engineering and design.Examples include distributed monitoring, mobile surface vehicles, and community-based water sampling.Source: §2.2 · p. 3

2.3 Citizen Science & Underwater Robotics

  • Existing underwater options leave a gap between low-cost access and scientific functionality, while some water-monitoring programs have limited measurement scope and precision.The paper notes missing pH and dissolved-oxygen readings and subjective rather than sensor-based turbidity assessments in some programs.Source: §2.3 · pp. 3–4
  • Jar Jar ROV combines educational robotics and participatory monitoring, using a yearly program in which middle-school students collect data from local lakes.The authors position it between high-cost, high-expertise systems and low-cost, low-functionality educational kits.Source: §2.3 · p. 4

3 Hardware & Software Design

The design pairs a repairable PVC ROV frame with a modular sensor pod that can also operate apart from the vehicle. Its electronics combine compact computing, cloud connectivity, battery power, and calibrated water-quality sensors.

3.1 ROV Design

  • The ROV uses an off-the-shelf propulsion kit on a custom PVC frame costing under $50, with foam for buoyancy control and stable sensor deployment.The frame geometry keeps the center of gravity below the center of buoyancy while carrying the sensor pod.Source: §3.1 · p. 4, Figure 2 · p. 4

3.2 Sensor Pod

  • The self-sufficient sensor pod can mount on existing robotic platforms or surface vessels, or operate as a standalone monitoring station.Its 230 mm PVC housing uses a sliding electronics board and a threaded cap; the seal was tested to 10 meters for one hour.Source: §3.2 · p. 5, Figure 3 · p. 5

3.2.1 Computing Stack

  • A compact Particle Boron-based computing stack was selected to fit the housing and simplify setup; built-in LTE supports cloud connectivity and remote data access.The stack also includes GPS and SD logging, and the cloud interface supports remote monitoring, firmware updates, and real-time visualization.Source: §3.2.1 · pp. 5–6, Figure 4 · p. 6

3.2.2 Power System

  • A 3600 mAh battery powered the system for roughly four hours, while the Boron regulated and distributed power at 3.3V.The integrated regulation avoids additional power-conversion circuits.Source: §3.2.2 · p. 6

3.2.3 Sensor Suite

  • The modular sensor suite measures pH, temperature, turbidity, and dissolved oxygen, with calibration procedures specified for each sensor.Sensors connect through 12-bit analog-to-digital converters and can be replaced or calibrated individually without affecting the whole system.Source: §3.2.3 · p. 6

4 Experimental Design

The study evaluates the Jar Jar ROV through coached instruction and lake trials with student teams. Its design pairs accessible parameter selection and structured training with a distributed deployment and data-upload workflow.

4.1 Overview

  • The validation combined curriculum instruction with coordinated field testing at multiple lake sites across a US state.Lake choices prioritized accessibility and educational value for the pilot.Source: §4.1 · p. 7

4.1.1 Parameter Selection

  • Teams measured temperature, pH, oxygen content, and turbidity to balance conceptual accessibility with conservation relevance.Students were expected to conduct lake trials and compare results with control values collected earlier by experts.Source: §4.1.1 · p. 7

4.1.2 Educators & Participants

  • The study included 14 of 26 registered teams and over 100 participants, with teams of 4–7 students supervised by two adult coaches.Twelve teams deferred participation because of scheduling conflicts.Source: §4.1.2 · p. 7

4.1.3 Educational Materials

  • Educational materials covered water science and ROV construction, while a three-month sequence moved from water-quality foundations to building and guided field data collection.After deployment, teams uploaded measurements to a centralized project database as the citizen-science component.Source: §4.1.3 · pp. 7–8

5 Training & Field Testing

The program prepared educators and teams through in-person and online training, then used a standardized lake-deployment and data-upload protocol. Post-assessment surveys indicated knowledge gains and increased STEM interest.

5 Training & Field Testing

  • Educators received in-person training in ROV construction, electrical assembly, software, sensors, ballasting, and tether management, followed by online programming, connectivity, and calibration instruction.Figure 5 contrasts the in-person skills training with recorded online instruction.Source: §5 · p. 8, Figure 5 · p. 8
  • Teams tested equipment, conducted 30-minute underwater surveys across multiple depths, and uploaded measurements for cleaning and weekly addition to the project website.The sensor pod logged raw data every two seconds and sent cumulative values to the website every ten seconds when able.Source: Figure 6 · p. 8, §5 · p. 9
  • Post-assessment surveys indicated knowledge gains in environmental science and robotics, increased STEM interest, and educational effectiveness through authentic citizen science participation.The program also validated the technical feasibility of student-operated environmental monitoring systems.Source: §5 · p. 9

6 Results

The results section introduces analyses of student-collected measurements and mentor feedback, including engagement differences across program activities. Its mentor survey also collected background, preparation, and kit-building feedback.

6 Results

  • The results overview identifies low data retention and an engagement gap in technical tasks as areas for improvement.The section analyzes student-collected measurements and mentor feedback.Source: §6 · p. 9

6.1 Educational Outcomes

ROV Kit & Building Process

  • Survey responses indicated strongest engagement in ROV construction and water-quality data collection, with lower engagement in sensor-pod assembly and data upload.Figure 7 indicates that technical ease followed the same pattern as engagement; higher values indicate favorability.Source: Figure 7 · p. 10

Curriculum & Water Quality Booklet

The survey questions covered the Water Quality Activity Booklet and student outcomes, but the supplied passages report questions rather than responses.

Curriculum & Water Quality Booklet

  • Mentors were asked about booklet alignment with student comprehension, how often teams used it, and which activities students found engaging.The questions also asked how booklet engagement compared with robotics building.Source: Curriculum & Water Quality Booklet · p. 10
  • The survey asked whether the booklet helped explain water-quality concepts and increased students’ understanding of local environmental issues..Source: Curriculum & Water Quality Booklet · p. 10

Student Outcomes

  • Mentors were asked to assess student learning in water quality, robotics, teamwork, and citizen science, alongside changes in STEM or environmental-science interest.Source: Student Outcomes · p. 10

Overall Feedback & Future Participation

Mentor surveys revealed uneven engagement and technical barriers, while the project retained thousands of usable water-quality samples. The paper also reports the program’s costs and potential for reuse and wider deployment.

Overall Feedback & Future Participation

  • Engagement averaged 5.0 for ROV construction and 4.8 for water-quality data collection, versus 2.5 for sensor-pod assembly and 1.8 for data upload.Technical ease and engagement were strongly positively correlated (r = 0.90, p < 0.01); seven teams reported electronics-related difficulties.Source: Overall Feedback & Future Participation · p. 11
  • All responding educator teams reported implementation challenges, while half were very satisfied and half somewhat satisfied with the experience.Six teams expressed definitive interest in future participation and two expressed conditional interest.Source: Overall Feedback & Future Participation · p. 11

6.2 Citizen Science Impact

  • 10,600 of more than 30,000 raw sensor measurements were retained as usable samples (32.8%); averaging samples into a daily value raised retention to 100%.Figure 8 shows sample points on the interactive public map; the ROV provides ground-truth readings complementary to averaged satellite measurements.Source: §6.2 · p. 11, Figure 8 · p. 12

6.3 Cost-Effectiveness and Scalability

  • The sensor kit costs $290 and the program roughly $1,000 per team, about one-sixth the cost of existing programs; the reusable kit and standardized curriculum indicate scalability potential.Table 1 compares example open-source and commercial ROV platform costs.Source: Table 1 · p. 11, §6.3 · p. 12

7 Pilot Program Insights

The pilot exposed hardware and data-quality issues alongside strong collection capacity, and feedback is guiding changes to hardware and curriculum. Plans target more reliable equipment and clearer, more accessible instruction.

7 Pilot Program Insights

  • The pilot year’s feedback and survey and log data will guide a comprehensive enhancement phase before the program’s planned repetition in May 2026.The authors describe the initial year as a pilot intended to uncover issues.Source: §7 · p. 12

7.1 Hardware Reliability Analysis

  • The turbidity sensor had a 57.1% failure rate in the original design; improved sealing eliminated subsequent failures, while two user-error pod leaks were omitted from Table 2.The two leaks followed teams missing the sealing step; both teams received replacements.Source: §7.1 · p. 13, Table 2 · p. 13

7.2 Data Collection Performance

  • 10,600 samples were retained for analysis from over 30,000 collected, a 32.8% retention rate after manual cleaning.Powered logging captured data outside target environments, and outliers also required removal.Source: §7.2 · p. 13

7.3 Curriculum and Training Improvements

  • Planned curriculum revisions address disconnected, overwhelming materials with difficulty ratings, time estimates, hands-on activities, videos, and clearer links to scientific impact.The lake curriculum was also described as too academic for a summer program.Source: §7.3 · pp. 13–14

8 Conclusion

The paper presents and validates a cost-effective ROV platform for water-quality citizen science with middle school students. Its pilot generated validated measurements and positive educational outcomes, while feedback informs improvements for future deployment.

8 Conclusion

  • Student teams collected 10,600 validated water-quality measurements, and the pilot cost was $300 per unit.The paper reports high engagement, particularly in hands-on construction and data collection.Source: §8 · p. 14
  • The pilot demonstrated the technical feasibility and educational effectiveness of the Jar Jar ROV with middle school students.The paper reports substantial knowledge gains and high engagement, especially in hands-on activities.Source: §8 · p. 14
  • Feedback informs a roadmap to unify the guide, improve hardware robustness, and refine the curriculum and user experience.The authors describe the pilot as validating the potential for scalable grassroots environmental monitoring infrastructure.Source: §8 · p. 14

9 Future Work & Modifications

The planned modifications respond to pilot failures and learning barriers, aiming to improve reproducibility, reduce technical complexity, and increase robustness. Hardware and instructional revisions are intended to make the system easier to build and reproduce.

9.1.1 Youth Leader Feedback

  • Mentors identified internal wiring as the main sensor-pod assembly friction point; soldering improved reliability but increased build complexity and skill demands.Tight clearance could pull connections loose, and affected teams could not diagnose the problem without disassembling the pod.Source: §9.1.1 · p. 15

9.1.2 Component Reliability Analysis

  • The original turbidity sensor had a 57.1% failure rate across 14 deployed units, while an improved seal eliminated subsequent failures.The authors qualify other components’ reliability by the limited team sample size (n = 14).Source: §9.1.2 · p. 15

9.2.1 Wider Housing

  • The redesign replaces the 2-inch tube with a 3-inch housing, providing clearance for cables and plug-in connectors while limiting soldering to the power-management sub-circuit.The larger bore is intended to prevent mechanical dislodgment during insertion or underwater operation.Source: §9.2.1 · pp. 15–16

9.2.2 Board-Mounted Component Layout

  • All internal components are now screwed to the MDF board, and cabling is routed beneath it to improve structural stability and connection resilience.The layout is intended to remain visible and easy to inspect during the build.Source: Figure 10 · p. 15, §9.2.2 · p. 16

9.2.3 Simplified Board Geometry

  • The simplified MDF board can be reproduced with a handheld drill or a school workshop bandsaw, without specialist laser-cutting equipment.The new outline is designed to make local fabrication and self-sourcing straightforward.Source: §9.2.3 · p. 16

9.3 Training Material Revisions

  • Revised LEGO-style instructions use pictorial steps with visible intermediate outcomes, task times, and difficulty ratings to address low engagement during assembly.Sensor-pod assembly engagement averaged 2.5 in the pilot.Source: §9.3 · p. 16

9.4 Lowering the Barrier to Entry

  • Together, the revisions aim to lower the entry threshold through less soldering, more robust construction, school-workshop fabrication, and clearer pictorial instructions.The paper positions these changes as supporting school teams and citizen-science groups beyond its direct support network.Source: §9.4 · pp. 16–17
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