|Title:||SmartWheels for Hands-on Physical Science|
|Principal Investigator:||Roozeboom, Clifton||Awardee:||Myriad Sensors, Inc. (Doing business as PocketLab)|
|Program:||Small Business Innovation Research [Program Details]|
|Award Period:||2 years (05/15/2022 – 05/14/2024)||Award Amount:||$1,000,000|
|Type:||Phase II Development||Award Number:||91990022C0039|
Company Website: www.thepocketlab.com
Video Demonstration of the Phase I Prototype: https://www.youtube.com/watch?v=kSm5ewKxrjY
Purpose: In this project, the team will fully develop a product where students play with a matchbox-sized car that includes a sensor that tracks scientific data. Speed, velocity, and acceleration are physics concepts that are relatively intuitive because students have some innate understanding from their personal experiences, such as riding a bike or going on a roller coaster. However, experimenting with speed and other motion concepts in the science classroom remains difficult and current solutions often do not align to student learning needs.
Project Activities: During Phase I in 2021 the team developed a prototype of a matchbox-sized car embedded with a sensor to capture and transmit scientific data to a digital notebook that presents data from which educators and students can draw insights. At the end of Phase I, researchers completed a pilot study with two middle school science teachers and 35 students to test the usability, feasibility, and promise of the prototype. The team found that the prototype captured student play data and transmitted the data to a dashboard that students and educators were able to view. Students found the prototype easy to use and indicated that the exercises were focused on learning objectives. Educators reported that students were engaged when using the prototype and were able to analyze data generated by the prototype.
In Phase II of the project, the team will fully develop the product, including a data visualization dashboard, a competition platform, and ten physical science classroom instructional modules. The team will conduct iterative refinements with feedback from educators and students at major production milestones until the product is fully functional. After development concludes, researchers will carry out a pilot study to test the feasibility and usability, fidelity of implementation, and the promise of the product for improving science learning. The team will collect data from 30 middle school science classes, with half randomly assigned to use the product and the other half to use business-as-usual activities for the same course content. Researchers will compare pre-and-post scores for NGSS-based items including analyzing and interpreting data from graphs, as well as student motivation for engaging in STEM activities and careers. Researchers will gather cost information using the "ingredients method" and will include all expenditures on things such as personnel, facilities, equipment, materials, and training.
Product: G-Force will be a physics learning product that embeds a sensor within a matchbox-sized car to collect scientific data (position, velocity, acceleration, g-forces, and compass heading) to encourage hands-on learning as middle school students play. The data collected with G-Force will be transmitted via Bluetooth to an existing cloud-based notebook that was developed through a prior ED/IES SBIR award. The intervention will include tracks for the cars and points to attach weights, bumpers, springs, velcro, hooks, and other accessories for experiments. The product is designed to be integrated with lessons in middle school physical science classes for topics aligned to Next Generation Science Standards, and will support the use of data visualizations, competitions, and a collaboration platform for races and design challenges.
Related IES Projects: SmartWheels for Hands On Physical Science (91990021C0026); AI-Driven Formative Assessments for Hands-on Science (91990021C0035); AI-Driven Formative Assessments for Hands-On Science (91990020C0073); CloudLab: Software Development for Hands-On Science Learning (EDIES17C0042); CloudLab Software for Hands-On Science Learning (99190018C0016)