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Introduction to Volume 13 Issue 1
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 13 Issue 1 — April 2022
Contents
Scientific Skills, Identity, and Career Aspiration Development from Early Research Experiences in Computer Science
Cecilia O. Alm and Reynold Baileypp. 2–16
https://doi.org/10.22369/issn.2153-4136/13/1/1BibTeX
@article{jocse-13-1-1,
author={Cecilia O. Alm and Reynold Bailey},
title={Scientific Skills, Identity, and Career Aspiration Development from Early Research Experiences in Computer Science},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={2--16},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/1}
}
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The computer science research workforce is characterized by a lack of demographic diversity. To address this, we designed and evaluated an end-to-end mentored undergraduate research intervention to nurture diverse cohorts' skills for research and develop their vision of themselves as scientists. We hypothesized that this intervention would (a) grow scientific skills, (b) increase science identity, and (c) stimulate students to view scientific careers in computer science as future viable options. The evaluation of the hypotheses addressed the limitations in self-evaluation with a multicomponent evaluation framework, comprising five forms of evidence from faculty and students, engaging on team projects, with cohorts additionally participating in professional development programming. Results indicated that students gained in scientific skills and broadened their identity as scientists and, to some degree, strengthened their outlook on research careers. The introduced structured intervention and evaluation framework were part of a US National Science Foundation Research Experiences for Undergraduates (REU) computing-focused summer program at Rochester Institute of Technology and are applicable in other scientific disciplines and institutional settings.
Creating a Graphical Tool for Non-Programmers to Use to Make Heatmaps
Nicholas Alicea, Akenpaul Chani, Lam Le, Hayata Suenaga, David Toth, Selam Van Voorhis, and Jessica Wootenpp. 17–20
https://doi.org/10.22369/issn.2153-4136/13/1/2BibTeX
@article{jocse-13-1-2,
author={Nicholas Alicea and Akenpaul Chani and Lam Le and Hayata Suenaga and David Toth and Selam Van Voorhis and Jessica Wooten},
title={Creating a Graphical Tool for Non-Programmers to Use to Make Heatmaps},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={17--20},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/2}
}
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Heatmaps are used to visualize data to enable people to quickly understand them. While there are libraries that enable programmers to create heatmaps with their data, scientists who do not typically write programs need a way to quickly create heatmaps to understand their data and use those figures in their publications. One of the authors is not a programmer but needed a way to generate heatmaps for their research. For a summer undergraduate research experience, we created a program with a graphical user interface to allow non-programmers, including that author, to create heatmaps to visualize their data with just a few mouse clicks. The program allows the user to easily customize their heatmaps and export them as PNG or PDF files to use in their publications.
Magic Castle — Enabling Scalable HPC Training through Scalable Supporting Infrastructures
Félix-Antoine Fortin and Alan Ó Caispp. 21–22
https://doi.org/10.22369/issn.2153-4136/13/1/3BibTeX
@article{jocse-13-1-3,
author={F\'{e}lix-Antoine Fortin and Alan \'{O} Cais},
title={Magic Castle \textemdash Enabling Scalable HPC Training through Scalable Supporting Infrastructures},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={21--22},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/3}
}
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The potential HPC community grows ever wider as methodologies such as AI and big data analytics push the computational needs of more and more researchers into the HPC space. As a result, requirements for training are exploding as HPC adoption continues to gather pace. However, the number of topics that can be thoroughly addressed without providing access to actual HPC resources is very limited, even at the introductory level. In cases where access to production HPC resources is available, security concerns and the typical overhead of arranging for account provision and training reservations make the scalability of this approach challenging.
Best Practices for NERSC Training
Yun (Helen) He and Rebecca Hartman-Bakerpp. 23–26
https://doi.org/10.22369/issn.2153-4136/13/1/4BibTeX
@article{jocse-13-1-4,
author={Yun (Helen) He and Rebecca Hartman-Baker},
title={Best Practices for NERSC Training},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={23--26},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/4}
}
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The National Energy Research Supercomputing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL) organizes approximately 20 training events per year for its 8,000 users from 800 projects, who have varying levels of High Performance Computing (HPC) knowledge and familiarity with NERSC's HPC resources. Due to the novel circumstances of the pandemic, NERSC began transforming our traditional smaller-scale, on-site training events to larger-scale, fully virtual sessions in March 2020. We treated this as an opportunity to try new approaches and improve our training best practices. This paper describes the key practices we have developed since the start of this transformation, including considerations for organizing events; collaboration with other HPC centers and the DOE ECP Program to increase reach and impact of events; targeted emails to users to increase attendance; efficient management of user accounts for computational resource access; strategies for preventing Zoombombing; streamlining the publication of professional-quality, closed-captioned videos on the NERSC YouTube channel for accessibility; effective communication channels for Q&A; tailoring training contents to NERSC user needs via close collaboration with vendors and presenters; standardized training procedures and publishing of training materials; and considerations for planning HPC training topics. Most of these practices will be continued after the pandemic as effective norms for training.
Building a Computational and Data Science Workforce
Katharine Cahill, Linda Akli, Tandabany Dinadayalane, Ana Gonzalez, Raphael D. Isokpehi, Asamoah Nkwanta, Rachel Vincent-Finley, Lorna Rivera, and Ahlam Tannouripp. 27–31
https://doi.org/10.22369/issn.2153-4136/13/1/5BibTeX
@article{jocse-13-1-5,
author={Katharine Cahill and Linda Akli and Tandabany Dinadayalane and Ana Gonzalez and Raphael D. Isokpehi and Asamoah Nkwanta and Rachel Vincent-Finley and Lorna Rivera and Ahlam Tannouri},
title={Building a Computational and Data Science Workforce},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={27--31},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/5}
}
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Under-representation of minorities and women in the STEM workforce, especially in computing, is a contributing factor to the Computational and Data Science (CDS) workforce shortage. In 2019, 12 percent of the workforce was African American, while only 7 percent of STEM workers were African American with a bachelor's degree or higher. Hispanic share of the workforce increased to 18 percent by 2019; Hispanics with a bachelor's degree or higher are only 8 percent of the STEM workforce [1]. Although some strides have been made in integrating CDS competencies into the university curriculum, the pace of change has been slow resulting in a critical shortage of sufficiently qualified students at both the baccalaureate and graduate levels. The NSF Working Group on Realizing the Potential of Data Science final report recommends "strengthening curriculum at EPSCoR and Minority Serving Institutions (MSI) so students are prepared and competitive for employment opportunities in industry and academia" [2]. However, the resource constraints and large teaching loads can impede the ability of MSIs and smaller institutions to quickly respond and make the necessary curriculum changes. Ohio Supercomputer Center (OSC) in collaboration with Bethune Cookman University (B-CU), Clark Atlanta University (CAU), Morgan State University (Morgan), Southeastern Universities Research Association (SURA), Southern University and A&M College (SUBR), and the University of Puerto Rico at Mayagüez (UPRM) are piloting a Computational and Data Science Curriculum Exchange (C2Exchange) to address the challenges associated with sustained access to computational and data science courses in institutions with high percentage enrollment of students from populations currently under-represented in STEM disciplines. The goal of the C2Exchange pilot is to create a network for resource constrained institutions to share CDS courses and increase their capacity to offer CDS minors and certificate programs. Over the past three years we have found that the exchange model facilitates the sharing of curriculum and expertise across institutions for immediate implementation of some courses and long-term capacity building for new Computational and Data Science programs and minors.
Tailored Computing Instruction for Economics Majors
Richard Lawrence, Zhenhua He, Wesley Brashear, Ridham Patoliya, Honggao Liu, and Dhruva K. Chakravortypp. 32–37
https://doi.org/10.22369/issn.2153-4136/13/1/6BibTeX
@article{jocse-13-1-6,
author={Richard Lawrence and Zhenhua He and Wesley Brashear and Ridham Patoliya and Honggao Liu and Dhruva K. Chakravorty},
title={Tailored Computing Instruction for Economics Majors},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={32--37},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/6}
}
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Responding to the growing need for discipline-specific computing curricula in academic programs, we offer a template to help bridge the gap between informal and formal curricular support. Here, we report on a twenty-contact-hour computing course developed for economics majors at Texas A&M University. The course is built around thematic laboratories that each include learning objectives, learning outcomes, assignments, and assessments and is geared toward students with a high-school level knowledge of mathematics and statistics. Offered in an informal format, the course leverages the wide applicability of the Python programming language and scaffolding offered by discipline-specific, hands-on activities to introduce a curriculum that covers introductory topics in programming while prioritizing approaches that are more relevant to the discipline. The design leverages technology to offer classes in an interactive, Web-based format for both in-person and remote learners, ensuring easy access and scalability to other institutions as needed. To ensure easier adoption among faculty and offer differentiated learning opportunities for students, lectures are modularized to 10-minute segments that are mapped to other concepts covered during the entire course. Class notes, lectures, and exercises are pre-staged and leverage aspects of flipped classroom methods. The course concludes with a group project and follow-on engagements with instructors. In future iterations, curriculum can be extended with a capstone in a Web-based asynchronous certification process.
Leveraging Northeast Cyberteam Successes to Build the CAREERS Cyberteam Program: Initial Lessons Learned
Andrew Sherman, John Goodhue, Julie Ma, Kaylea Nelson, Eric Brown, Christopher Carothers, Galen Collier, Adrian Del Maestro, Andrea Elledge, Wayne Figurelle, John Huffman, Gaurav Khanna, Neil McGlohon, Sia Najafi, Jeff Nucciarone, Anita Schwartz, Bruce Segee, Scott Valcourt, and Ralph Zottolapp. 38–43
https://doi.org/10.22369/issn.2153-4136/13/1/7BibTeX
@article{jocse-13-1-7,
author={Andrew Sherman and John Goodhue and Julie Ma and Kaylea Nelson and Eric Brown and Christopher Carothers and Galen Collier and Adrian Del Maestro and Andrea Elledge and Wayne Figurelle and John Huffman and Gaurav Khanna and Neil McGlohon and Sia Najafi and Jeff Nucciarone and Anita Schwartz and Bruce Segee and Scott Valcourt and Ralph Zottola},
title={Leveraging Northeast Cyberteam Successes to Build the CAREERS Cyberteam Program: Initial Lessons Learned},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={38--43},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/7}
}
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Given the pivotal role of data and cyberinfrastructure (CI) in teaching and scientific discovery, it is essential that researchers at small and mid-sized institutions be empowered to fully exploit them. While access to physical infrastructure is essential, it is equally important to have access to people known as Research Computing Facilitators (RCFs) who possess a mix of technical knowledge and interpersonal skills that enables faculty to make the best use of available computing resources. Meeting this need is a significant challenge for small and mid-sized institutions that do not have the critical mass to build teams of RCFs on site. Launched in 2017, the National Science Foundation (NSF) funded Northeast Cyberteam (NECT) built a program to address these challenges for researchers/educators at small and mid-sized institutions in four states — Maine, Massachusetts, New Hampshire, and Vermont — while simultaneously developing self-service tools that support management and execution of RCF engagements. These tools are housed in a Portal called Connect.cyberinfrastructure and have enabled adoption of program methods by the broader research computing community. Initiated in 2020, the NSF-funded Cyberteam to Advance Research and Education in Eastern Regional Schools (CAREERS) has leveraged the NECT methods and tools to jumpstart a program that supports researchers at small and mid-sized institutions in six states and lays the groundwork for an additional level of support via a distributed network of experts directly accessible by the researchers in the region. This paper discusses findings from the first four years of NECT and the first year of CAREERS.
Technology Laboratories: Facilitating Instruction for Cyberinfrastructure Infused Data Sciences
Zhenhua He, Jian Tao, Lisa M. Perez, and Dhruva K. Chakravortypp. 44–49
https://doi.org/10.22369/issn.2153-4136/13/1/8BibTeX
@article{jocse-13-1-8,
author={Zhenhua He and Jian Tao and Lisa M. Perez and Dhruva K. Chakravorty},
title={Technology Laboratories: Facilitating Instruction for Cyberinfrastructure Infused Data Sciences},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={44--49},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/8}
}
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While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CI-training approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called "tech-labs," assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.
Expanding Interactive Computing to Facilitate Informal Instruction in Research Computing
Richard Lawrence, Tri M. Pham, Phi T. Au, Xin Yang, Kyle Hsu, Stuti H. Trivedi, Lisa M. Perez, and Dhruva K. Chakravortypp. 50–54
https://doi.org/10.22369/issn.2153-4136/13/1/9BibTeX
@article{jocse-13-1-9,
author={Richard Lawrence and Tri M. Pham and Phi T. Au and Xin Yang and Kyle Hsu and Stuti H. Trivedi and Lisa M. Perez and Dhruva K. Chakravorty},
title={Expanding Interactive Computing to Facilitate Informal Instruction in Research Computing},
journal={The Journal of Computational Science Education},
year=2022,
month=apr,
volume=13,
issue=1,
pages={50--54},
doi={https://doi.org/10.22369/issn.2153-4136/13/1/9}
}
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Successful outreach to computational researchers for informing about the benefits of switching to a different computing environment depends on the educator's ability to showcase practical research and development workflows in the new computing environment. Interactive, graphical computing environments are crucial to engage learners in computing education and offer researchers easier ways to adopt new technologies. Interactive, graphical computing allows learners to see the results of their work in real time, which provides the needed feedback for learning and enables chunking of complex tasks. Moreover, there is a natural synergy between computing education and computing research; researchers who are exposed to new computing skills within the context of an interactive and engaging environment are more likely to retain the new skills and adopt the new computing environment in their research and development workflows. Support for interactive, graphical workflows with modern computing tools in containerized computing environments has to be incorporated on high performance computing systems. To begin to address this deficiency, here we discuss our approach to teach containerization technologies in the popular integrated development environment of the Jupyter Notebook. We report on our scheme for implementing containerized software environments for interactive, graphical computing within the Open OnDemand (OOD) framework for research computing workflows, providing an accessible on-ramp for researchers transitioning to containerized technologies. In addition, we introduce several quality-of-life improvements for researchers and educators that will encourage them to continue to use the platform.