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MATLAB Curriculum Based in Experimental Setups with Authentic Data Collection and Analysis Experiences

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Experimentation and Laboratory-Oriented Studies Division Technical Session 3: Best of ELOS

Page Count

13

DOI

10.18260/1-2--41524

Permanent URL

https://peer.asee.org/41524

Download Count

442

Paper Authors

biography

Brian O'Connell Northeastern University

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Brian O'Connell is an Associate teaching professor in the First-Year Engineering program at Northeastern University. His undergraduate degree in Mechanical Engineering came from the University of Massachusetts at Amherst in 2006. He then worked for Kollmorgen Electro/Optical as a mechanical engineer developing periscopes and optronic masts. In 2011, he returned to academia at Tufts University, earning his MS and Ph.D. in Mechanical Engineering for his work with low-cost educational technologies and his development and use of technologies to aid usage tracking in makerspaces to examine them as interactive learning environments. He joined Northeastern in 2017. As well as teaching first-year engineering courses, he continues to design new technologies and curricula for use in his own classroom as well as for K-12 engineering education outreach.

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biography

Jada Wong

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A Mechanical Engineering student from Northeastern University with a minor in Architectural Engineering

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Abstract

This demonstration features a suite of experimental setups designed and developed for use in a first-year engineering course as part of their MATLAB instruction. Each experiment involves a portable data collection unit and baseline data collection software to enable initial interaction with the hardware. They purposefully showcase experimental setup and data collection as part of their MATLAB curriculum to learn the software in a setting and context more in-line with how they will use it in future courses. The suite currently contains hardware for collecting UV levels and GPS data, listening for signal transmission through a simulated noisy environment, recording hand-eye reaction times based on randomized stimuli, and a desktop mechanical stress test. The hardware enables students to collect the specific data they need for each experiment, containing all sensors and other necessary components in a rugged and compact self-contained unit. The size is such that they can easily transport to perform their experiments where convenient, like their dorm rooms. The cost of each averaged to less than $25 per student per group by taking advantage of low-cost systems like Arduino and rapid prototyping options typically available in most academic settings. Since the educational objective is to develop their skill and understanding in MATLAB, module assessment focuses on the quality of the scripts developed to analyze the collected data, not the quality of the experimentation or understanding of the underlying concepts. Those elements are heavily scaffolded though through provided software which guides them through the experimental runs, uploads their data point(s) to a database, and downloads the class dataset from that database. Students then apply their MATLAB lessons to organize and analyze the dataset by eliminating noise and extraneous data and visualization of findings.

O'Connell, B., & Wong, J. (2022, August), MATLAB Curriculum Based in Experimental Setups with Authentic Data Collection and Analysis Experiences Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41524

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