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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Related Experiment Video

Updated: Oct 18, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction.

Alexandra Gendreau Chakarov¹, Quentin Biddy², Colin Hennessy Elliott³

¹Computer Science and Science Education, San José State University, San Jose, CA 95125, USA.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

Students can use the Data Sensor Hub (DaSH) system with micro:bit to collect and analyze real-world data. This approach integrates computing into science education, fostering inquiry-based learning and scientific practices.

Keywords:

inquiry science education programmable sensors in education sensor use in education

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Area of Science:

Educational Technology
Physical Computing
STEM Education

Background:

Traditional science education often lacks hands-on data analysis tools for students.
Integrating computing into science curricula can enhance engagement and understanding of scientific phenomena.
There is a need for accessible, affordable, and user-friendly systems for K-12 data science education.

Purpose of the Study:

To describe the Data Sensor Hub (DaSH), a sensor-based physical computing system for students.
To present two inquiry-oriented instructional units designed for middle grade science classes using DaSH.
To explore how DaSH and its associated units can foster scientific and computational thinking in students.

Main Methods:

The Data Sensor Hub (DaSH) system utilizes the BBC micro:bit microcontroller with a gator:bit expansion.
Students program the system using a visual, cloud-based environment suitable for novices.
A variety of sensors (temperature, humidity, CO2, sound, acceleration, magnetism) are connected to collect data streams.

Main Results:

The DaSH system enables students to process, analyze, and visualize real-time sensor data.
Instructional units facilitate place-based learning and inquiry into scientific phenomena.
The system supports the development of computational thinking, data literacy, and scientific practices.

Conclusions:

The DaSH system provides an effective platform for integrating physical computing into middle grade science education.
Inquiry-based learning with DaSH promotes deeper student engagement and understanding of scientific concepts.
The system supports the development of authentic STEM practices, preparing students for future scientific and technical fields.