Best Practices of Student Engagement in Online Teaching of HPC and Big Data Research

dc.contributor.authorGobbert, Matthias
dc.contributor.authorWang, Jianwu
dc.date.accessioned2021-10-08T17:25:02Z
dc.date.available2021-10-08T17:25:02Z
dc.date.issued2021
dc.description.abstractThere is a critical nationwide shortage of IT professionals as well as of scientists and engineers with highperformance computing (HPC) and big data related advanced computing skills. Simultaneously, the technology is growing in complexity and sophistication, which has led to the use of multidisciplinary teams with members from a broad range of home domains everywhere in industry, government, and academia. Moreover, a lot of the vital team collaborations take will place virtually using a variety of software platforms now and in the future. We report here on experiences with preparing undergraduate and graduate students for these career opportunities in several contexts, from regular semester classes, an undergraduate summer research program, to an advanced graduate student CyberTraining program. All these programs are conducted fully online and leveraged concepts of flipped classrooms, recorded lectures, team-based and active learning, regular oral presentations, and more to ensure student engagement and lasting learning.en_US
dc.description.sponsorshipThis work is supported by the grant “REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering” from the National Science Foundation (grant no. OAC–2050943). This work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources” from the National Science Foundation (grant no. OAC–1730250). The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS– 1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See https://hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttps://hpcf-files.umbc.edu/research/papers/Gobbert_EduHPC2021.pdfen_US
dc.format.extent6 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2dzak-uwwc
dc.identifier.citationGobbert, Matthias; Wang, Jianwu; Best Practices of Student Engagement in Online Teaching of HPC and Big Data Research; High Performance Computing Facility, 2021; https://hpcf-files.umbc.edu/research/papers/Gobbert_EduHPC2021.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/23072
dc.language.isoen_USen_US
dc.publisherUMBC High Performance Computing Facilityen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Information Systems Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleBest Practices of Student Engagement in Online Teaching of HPC and Big Data Researchen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-1745-2292en_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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