Evaluating the Effectiveness of an Online Learning Platform in Transitioning Users from a High Performance Computing to a Commercial Cloud Computing Environment

The COVID-19 national health crisis forced a sudden and drastic move to online delivery of instruction across the nation. This almost instantaneous transition from a predominantly traditional "inperson" instruction model to a predominantly online model has forced programs to rethink instructional approaches. Before COVID-19 and mandatory social distancing, online training in research computing (RC) was typically limited to "live-streaming" informal in-person training sessions. These sessions were augmented with hands-on exercises on live notebooks for remote participants, with almost no assessment of student learning. Unlike select instances that focused on an international audience, local training curricula were designed with the in-person attendee in mind. Sustained training for RC became more important since when several other avenues of research were diminished. Here we report on two educational approaches that were implemented in the informal program hosted by Texas A&M High Performance Research Computing (HPRC) in the Spring, Summer, and Fall semesters of 2020. These sessions were offered over Zoom, with the instructor assisted by moderators using the chat features. The first approach duplicated our traditional in-person sessions in an online setting. These sessions were taught by staff, and the focus was on offering a lot of information. A second approach focused on engaging learners via shorter pop-up courses in which participants chose the topic matter. This approach implemented a peer-learning environment, in which students taught and moderated the training sessions. These sessions were supplemented with YouTube videos and continued engagement over a community Slack workspace. An analysis of these approaches is presented.

Section: Challenges and Future Workmentioning

confidence: 98%

Section: Online Training and Educationmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Remote Learning Methods for User Training in Research Computing

Chakravorty¹,

Pérez²,

Liu³

et al. 2021

“…Furthermore, "quality of life" features that are common in AI/ML toolkits on public cloud-based environments are often not available on these resources. We have previously reported on our work in advancing the state of CI-training at Texas A&M. Texas A&M High Performance Research Computing (HPRC) offers over sixty training seminars, courses, workshops, and week-long computing-camps that support CI aspirations ranging from middle school students to CI professionals [3][4][5][6][7][8][9][10]. During the COVID-19 pandemic-imposed workplace changes, we have learned that pedagogical approaches commonly adopted in in-person environments do not translate to a virtual learning setting.…”

Section: Introductionmentioning

confidence: 99%

Technology Laboratories: Facilitating Instruction for Cyberinfrastructure Infused Data Sciences

He¹,

Ji²,

Pérez³

et al. 2022

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 CItraining 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.

“…Texas A&M High Performance Research Computing has abundant experience developing informal courses and pedagogical approaches tailored for specific user groups [3][4][5][6][7][8][9][10]. These courses cover a wide scope of topics including introductions to our HPC systems and Linux basics; programming in Python, R, Matlab, and Pearl; and bioinformatics.…”

Section: Introductionmentioning

confidence: 99%

Bridging Data Science Programming with Advanced Formal Coursework

Brashear¹,

He²,

Lawrence³

et al. 2022

In order to fulfill the needs of an evolving job market, formal academic programs are continuously expanding computational training in traditional discipline-specific courses. We developed an informal, twelve contact-hour course tailored for economics students entering a computationally rigorous graduate-level course to help mitigate disparities in computing knowledge between students and prepare them for more advanced instruction within the formal setting. The course was developed to teach the R programming language to students without assuming any prior knowledge or experience in programming or the R environment. In order to allow for ease of implementation across various training approaches, the course was modularized with each section containing distinct topics and learning objectives. These modules can be easily developed as independent lessons so that disciplinespecific needs can be addressed through inclusion or exclusion of certain topics. This implementation used the R package 'learnr' to develop the course, which rendered a highly extensible and portable interactive Shiny document that can be deployed on any system on which RStudio is installed. The course is offered as a series of interactive sessions during which students are led through the Shiny notebook by an instructor. Owing to its structure, it can be offered as an asynchronous web-based set of tutorials as well.