Paper ID #31869Incorporating Design in Electronics LaboratoriesDr. Jean-Michel I. Maarek, University of Southern California Jean-Michel Maarek is professor of engineering practice and director of undergraduate affairs in the De- partment of Biomedical Engineering at the University of Southern California. His educational interested include engaged learning, student assessment, and innovative laboratories American c Society for Engineering Education, 2020 Incorporating Design in Electronics LaboratoriesIntroduction and background:Engineering courses
IoT program between California Baptist University (CBU) andShanghai Aurora Vocational College (SAVC); the Overview of Course section presents thelearning objectives and assessment methods used in teaching this course; the Lab Modulessection lists the lab projects and design projects with descriptions and hardware/softwarespecifications; the Results and Discussions section discusses the effectiveness of the learn-by-doing approach and lessons learned.Background Over the past few years, engineering educators in North America have designed coursesand lab activities involving IoT technologies and integrated these components into existingengineering curricula. An IoT based Innovation Laboratory was created at Seattle University, aspart of
digital design course that hosted the second experiment in flexible assessments. Thetwo sections included 43 electrical engineering (EE) majors and five computer engineering(CPE) majors. The course topics included gate-level design, modular design, and finite statemachine-based circuit design. The course included 14 laboratory experiments in which studentslearned to model circuits using Verilog and implement them on an FPGA-based developmentboard. We enhanced the inclusive nature of the course by choosing no-cost courseware6,including textbooks, lab manual, and the HDL development environment. We taught the coursein a studio classroom using primarily a flipped format, which matches the format of our previousexperiments.Each quiz included a
instructionaltime and had the drawback that students tended to forget usage details between experiments,requiring further expenditures of class time. Further, students worked on the in-class Simulinklabs in teams of two, a method which could not guarantee that each student actively gainedexperience with Simulink. The OER grant resulted in the creation of four openly accessibleSimulink-based laboratory experiments with detailed instructions5 and an accompanying videointroduction to Simulink. These lab experiments were deployed in the Fall 2018 semester andrequired each individual student to view the video tutorial, complete the lab experiments on hisor her own – outside of class time, and submit a lab report. This individual student engagementwith Simulink
Genomics Databases. (2016, October 11). Laboratory Equipment, p. n/a. Retrieved from http://search.proquest.com/docview/1830956349 [4] Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2018). Machine Bias. Nieman Reports, 72(3/4). Retrieved from http://search.proquest.com/docview/2136026806/ [5] West, S.M., Whittaker, M. & Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. AI Now Institute. Retrieved from https://ainowinstitute.org/discriminatingsystems.htm [6] Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn (Vol. 11). Washington, DC: National academy press. https://www.nap.edu/read/10129/chapter/8#118 [7] CIGI-Ipsos 6. (2019). 2019
for funding the project and providing the opportunity for the Cal Poly Pomonaengineering students to participate is such a rewarding endeavor.References 1. Grau, A., Indri, M., LoBello, L., Sauter, T., “Industrial Robotics in Factory Automation: from the Early Stage to the Internet of Things,” 43rd IEEE Industrial Electronics Conference IECON, Japan 2017. 2. Verner, I. and Gamer, S., “Reorganizing the Industrial Robotics Laboratory for Spatial Training of Novice Engineering Students,” Proceedings International Conference on Interactive Collaborative Learning, Florence Italy, 2015.3. Chang, G. and Stone, W., “An Effective Learning Approach for Industrial Robot Programming”, 120th ASEE Annual Conference &