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Embedded Systems Learning Using Current Technical Platforms

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Technical Session 2: Embedded Systems

Tagged Division

Computers in Education

Page Count

9

DOI

10.18260/1-2--32701

Permanent URL

https://peer.asee.org/32701

Download Count

295

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Paper Authors

biography

Yul Chu University of Texas Rio Grande Valley

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Dr. Yul Chu is an Associate Professor in the Department of Electrical Engineering at the University of Texas Rio Grande Valley. He received his Ph.D. in Electrical and Computer Engineering from the University of British Columbia, Canada in 2001 and MS in Electrical engineering from Washington State University in 1995. His current research interests lie in the area of low-power embedded systems, high-performance computing, parallel processing, cluster and high-available architectures, computer networking, digital system design, etc.

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biography

Jin H. Park Computer Science Department, California State University, Fresno, CA

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Jin H. Park is an associate professor in the Department of Computer Science at California State University, Fresno, CA, USA.
He received his M.S. and Ph.D. degrees in Computer Science from The Ohio University, Athens, OH, USA and Oklahoma State University, Stillwater, OK, USA in 1987 and 1998, respectively.
His research interests include high performance computing, parallel and distributed processing, bioinformatics and computational biology, and embedded systems.

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Abstract

This paper describes the study of embedded system hands-on labs, which can provide students to explore current technical issues for hardware and software design. The pedagogical approach for this paper is based on the team-based learning. This paper recommends using one or two popular boards, such as Raspberry Pi and Arduino, as embedded system platforms. The Raspberry Pi is a small (900 MHz quad-core ARM Cortex-A7 CPU) single-board computer developed by the Raspberry Pi Foundation and Arduino is the most popular microcontroller (based on the ATmega328), which is a flexible and easy-to-use hardware and software, for doing various embedded systems projects. Both platforms have been popular for many embedded system projects, such as home automation, IoT (Internet of Things), robots, games, and servers. This paper proposes an evidence-based practice using three class modules in sequence. Those are 1) Module 1: teaching approach (e.g., 6 or 7 lectures) to learn basic concepts and operations for the platforms; 2) Module 2: instructional technologies regarding how to implement labs and projects; and 3) Module 3: institutional strategies to support each team to design and implement team projects successfully. The outcomes will be rated by three factors, such as measurement of three modules, student feedback, and career development status. Firstly, the measurement of three basic modules are as follows: 1) Module 1 measurement: one (or two) exam(s) and teaching evaluation for the several lectures; 2) Module 2 measurement: report grading for each lab and project including demonstration; and 3) Module 3 measurement: grading for final reports, presentations, and demonstrations. Secondly, the paper provides several questions to get honest feedback and comments from students regarding how they comprehend the embedded systems learning based on hands-on labs and projects. Then, this paper analyzes those feedbacks to enhance the contents of three modules as a future work. Lastly, for career development status, this paper suggests collecting the statistics for the internships and permanent job achievements for juniors and graduating seniors. To enhance the effectiveness of the outcomes, this paper discusses the module materials and education methods for the future embedded system learning based on the assessment results for the above three factors.

Chu, Y., & Park, J. H. (2019, June), Embedded Systems Learning Using Current Technical Platforms Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32701

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