RHLab: Digital Inequalities and Equitable Access in Remote Laboratories

Inonan Moran, Marcos; Paul, Animesh; May, Dominik; Hussein, Rania

doi:10.18260/1-2--44150

2023 ASEE Annual Conference &Amp; Exposition Proceedings

DOI: 10.18260/1-2--44150

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RHLab: Digital Inequalities and Equitable Access in Remote Laboratories

Marcos Inonan Moran,

Animesh Paul,

Dominik May

et al.

Abstract: in Seattle. His research is centered on developing remote laboratories with a lens of equitable access to engineering education, and driven by his commitment to promote diversity, equity and inclusion in STEM education. In addition to his research on remote laboratories, Marcos has expertise in digital communication theory, signal processing, radar technology, and firmware engineering. Additionally, he has extensive experience in teaching embedded systems and senior design courses.

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MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology

Inonan,

Hussein

2023

IEEE Access

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Studies have emphasized the pivotal role of remote laboratories in delivering high-quality engineering education. Specifically, within the domain of wireless communication, remote laboratories offer students the opportunity to actively engage in practical Radio Frequency (RF) experiments, much like their traditional in-person counterparts. Through online platforms, students can construct RF prototypes, gaining handson experience in communication theories. However, the lack of a standardized model for designing and evaluating remote labs in RF education limits their consistency and effectiveness. This paper introduces MELODY, a model and classification framework tailored explicitly for Software Defined Radio (SDR) remote laboratories. This model is characterized by its technology-agnostic and open-source approach. The paper evaluates existing SDR remote lab projects, analyzing their architecture and design choices. MELODY is presented in details, encompassing process, services, platform, and infrastructure layers. Additionally, MELODY provides a classification framework, assigning ratings to SDR remote labs on a 1 to 5-star scale aligned with engineering standards. The classification framework's rubric assesses isolation challenges, calibration, scalability, and remote SDR lab availability in order to compare with other SDR remote labs. Practical applications developed by the Remote Hub Lab (RHL), such as the remote labs RHL-RELIA and RHL-RADAR are explored, showcasing how MELODY can be effectively applied. MELODY aims to establish standardization, ensuring consistency and quality assurance in remote SDR labs, fostering innovation, skill development, and collaboration within engineering education.

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MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology

Inonan,

Hussein

2023

IEEE Access

Self Cite

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show abstract

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RHLab: Digital Inequalities and Equitable Access in Remote Laboratories

Cited by 1 publication

References 13 publications

MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology

MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology

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