Students will launch the NYC bin overflow project by examining a realistic sanitation problem, gathering initial evidence from multiple perspectives, and defining who is most affected. They will use brief data displays, observation cues, and structured peer feedback to produce a challenge statement, an end-user profile, and inquiry questions that guide later research, modeling, and prototype design.

Students will analyze NYC bin overflow patterns through source evaluation, community-informed research, mathematical modeling, and early concept development so they can identify root causes and justify a promising sensor-based monitoring approach.

Students will use bin-fill data, user needs, and rapid prototyping to design a sensor-based trash bin monitoring solution, write a testable prediction, plan their build, create and revise an early prototype, and document how mathematical models and feedback shaped their design choices.

Students will implement their sensor-based bin monitoring prototypes in a short trial, collect and analyze evidence, compare predicted and observed bin-fill patterns, and document how test results lead to a concrete design improvement before the final public sharing phase.

Students will present and evaluate how their sensor-based trash bin monitoring work addressed a real NYC sanitation need, using evidence from prototype testing, mathematical modeling, and community-informed design decisions to explain impact and next steps.