Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 108C (2017) 2119–2120
International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland
The Art of Teaching Computational Science The ArtAlfredo of Teaching Computational Science Tirado-Ramos1and Angela B. Shiflet2 1
University of Texas Health at San Antonio, San Antonio, TX, U.S.A. 1 2 Alfredo Tirado-Ramos and Angela B. Shiflet2 Wofford College, Spartanburg, SC, U.S.A. 1 University of Texas Health at San Antonio, San Antonio, TX, U.S.A.
[email protected],
[email protected] 2 Wofford College, Spartanburg, SC, U.S.A.
[email protected],
[email protected]
Abstract The Workshop on Teaching Computational Science (WTCS) presented innovative research work Abstract describing innovations in teaching computational science in its various aspects, e.g. modeling and The Workshop on Teaching Computational (WTCS) presented innovative research work simulation, high-performance and large-data Science environments, during the International Conference in Computational Science 2017. This editorial providesscience an introduction to theaspects, work presented during and the describing innovations in teaching computational in its various e.g. modeling sessions. high-performance and large-data environments, during the International Conference in simulation, Computational Science 2017. This editorial provides an introduction to the work presented during the © 2017 The Authors. Published by Elsevier B.V. Keywords: computational, informatics, parallel computing; e-Learning; collaborative environments sessions. teaching, Peer-review under responsibility of the scientific committee of the International Conference on Computational Science Keywords: teaching, computational, informatics, parallel computing; e-Learning; collaborative environments
1 Introduction relatively recently, it has become possible for institutions of higher learning to integrate 1 Only Introduction
educational programs, courses and modules in computational science and scientific computing into theirOnly academic curricula. While computational science used toofbehigher complicated andtoexpensive, relatively recently, it teaching has become possible for institutions learning integrate nowadays, integration of computational has becomescience more effective, allowing teachers into and educationalthe programs, courses and modulesmethods in computational and scientific computing their academic teaching computational sciencesettings. used to be complicated anduniversities expensive, students to posecurricula. original While questions in lower cost experimental Thus, schools and nowadays, thetheir integration of computational methods hastobecome more effective, allowing teachers and alike enhance students’ scientific computing skills solve scientific problems with mathematical studentsand to pose original analysis questionstechniques in lower cost experimental settings. Thus, and universities models quantitative developed with the help of highschools performance and high alike enhance their students’ scientific to solve scientific problems with mathematical computational power. Promoting the computing importanceskills of computational science instruction in science models and has quantitative with thethehelp of high and high classrooms become aanalysis priority,techniques and sciencedeveloped teachers around world have performance shared experiences on computational power. Promoting the importance instruction in blended science the use of high performance computing facilities, be of it incomputational the context ofscience self-directed learning, classrooms become alearning priority, environments. and science teachers the world shared instructors experiencesand on learning or has traditional In thearound past few years,have teachers, the use of high computing facilities, it in the context of self-directed blended professors haveperformance gradually learnt to leverage thebepotential of computational toolslearning, in introductory learning or traditional environments. In the past fewservice years, teachers, and programming classes, learning industry-related intensive trainings, courses, instructors and specialist undergraduate postgraduate topics, creating the virtual laboratories for in-silico tools experimentation and professors haveor gradually learnt to leverage potential of computational in introductory learning, by integrating methods from e-Learning, computer service science, courses, mathematical programming classes, industry-related intensive trainings, and modeling, specialist undergraduate postgraduate topics, creating for in-silico and simulation, andorscientific visualization, amongvirtual others.laboratories In this workshop, we experimentation discuss submissions learning, by on integrating methodsas from computer science, concentrating issues of current, well ase-Learning, local and international interest. mathematical modeling, simulation, and scientific visualization, among others. In this workshop, we discuss submissions concentrating on issues of current, as well as local and international interest. 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science 10.1016/j.procs.2017.05.278
Alfredo Tirado-Ramos et al. / Procedia Computer Science 108C (2017) 2119–2120
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2 The Workshop This year the workshop accepted ten papers, reflecting a varied sample of research work in the field. Hirst presents an overview of learning tools and provides several examples of modeling and simulation projects that have been used in mathematics courses at the secondary and college level. Shiflet et al describe the gaps in educational tools for teaching modeling and simulation in computational science courses. They present their wide ranging online toolbox and new textbook, which is the only of its kind designed specifically for an introductory course in the computational science and engineering curriculum, including applications for invasive species, rumor spreading, juvenile delinquency, fur patterns, condor populations, rice viruses, toxin producing micro-organisms, coral bleaching, brown bears, neuron signals, and succession. Gryazin’s contribution examines teaching high performance computing at an American regional university and tackles challenges and opportunities for curriculum development, focusing on the discussion of methods of optimization of serial codes and APIs, such as OpenMP, MPI and CUDA. Eichholz explores specs based grading and unit testing in an introductory scientific parallel computing course, presenting a unit-testing scheme that allows instructors and students alike to assess the efficiency and basic design of student codes, without the instructors’ obligation to personally read and test the students’ codes. Lamprecht et al introduce a new approach to use scientific workflows in computational science education, describing how a process modeling and execution framework for scientific workflow projects can be used in the scope of a computer science course for Master students with a background in natural sciences. Reuter et al summarize the particular pedagogical strategies for computational science education at Stony Brook University, presenting training domain experts who are skilled at using high-performance computational resources to generate, analyze, and interpret data to solve research problems. Cahill et al share their experiences and insights earned during the Blue Waters Program, which was designed to teach online graduate credit courses. Chozas et al describe their work with IBM Watson, a cognitive system built by IBM for the education of novice parallel programmers, and explain how their use of the IBM Watson’s dialogue service enabled them to develop a solution that assists programming beginners in avoiding common OpenMP mistakes. Humphreys et al introduce their new curriculum, which brings mathematical analysis, algorithm design, optimization, data science and mathematical modeling into the forefront of interdisciplinary study in the pure and applied sciences. They elaborate on the context of big data and high performance computing and new published textbook, while both their lab manuals and supporting materials will be openly accessible online. Finally, Kostur analyzes the web based mathematical experimentation environment of SageMath notebook (sagenb), which provides faculty and students with a central installation and helps to prepare teaching materials for most of courses; then discusses the introduction of the new Jupyter notebook version, which offers SageMath kernel and pure scientific Python ecosystem for scientific computing.
3 Conclusions The presentations discussed at WTCS 2017 illustrate the wide variety and scope of computational science education around the world. The continually growing interest in this workshop attests to the developing necessity of research in this important interdisciplinary area. For 2018 the organizers plan to further increase the latitude of the workshop, by including instances on bridging the growing high performance computing and data science talent gap. Acknowledgments. We would like to acknowledge P.M.A. Sloot and V. Krzhizhanovskaya for their continuous support and commitment to the success of this workshop.
