Best Practices of Student Engagement in Online Teaching of HPC and Big Data Research
dc.contributor.author | Gobbert, Matthias | |
dc.contributor.author | Wang, Jianwu | |
dc.date.accessioned | 2021-10-08T17:25:02Z | |
dc.date.available | 2021-10-08T17:25:02Z | |
dc.date.issued | 2021 | |
dc.description.abstract | There 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.sponsorship | This 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.uri | https://hpcf-files.umbc.edu/research/papers/Gobbert_EduHPC2021.pdf | en_US |
dc.format.extent | 6 pages | en_US |
dc.genre | journal articles | en_US |
dc.genre | preprints | en_US |
dc.identifier | doi:10.13016/m2dzak-uwwc | |
dc.identifier.citation | Gobbert, 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.pdf | en_US |
dc.identifier.uri | http://hdl.handle.net/11603/23072 | |
dc.language.iso | en_US | en_US |
dc.publisher | UMBC High Performance Computing Facility | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Mathematics Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Information Systems Department | |
dc.rights | This 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.subject | UMBC High Performance Computing Facility (HPCF) | |
dc.title | Best Practices of Student Engagement in Online Teaching of HPC and Big Data Research | en_US |
dc.type | Text | en_US |
dcterms.creator | https://orcid.org/0000-0003-1745-2292 | en_US |
dcterms.creator | https://orcid.org/0000-0002-9933-1170 | en_US |