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A Data Driven Approach to Informal HPC Training Evaluation

Published: 10 September 2023 Publication History

Abstract

The High Performance Computing (HPC) community has a long history of educating researchers, students, and practitioners to use HPC systems effectively and efficiently, primarily through informal, non-graded workshops and short courses. While nearly all workshop evaluations capture user satisfaction and perform pre- and post-workshop evaluations, to our knowledge none of them capture changes in user behavior as a means of evaluating learning. To more fully evaluate a student’s skill development and capture improvements in the use of HPC systems, we developed a data centric method for evaluating informal HPC training. The evaluation methodology is described in four steps: design clear learning objectives, map the learning objectives and content to the data collection instruments and queries of interest, collect the data, and explore and analyze the data. This is a process easily adaptable by any HPC center because we focus on data that are available to all HPC trainers, e.g. workshop surveys, questions, and job performance data from the HPC system, primarily from an HPC scheduler.

References

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Published In

cover image ACM Conferences
PEARC '23: Practice and Experience in Advanced Research Computing 2023: Computing for the Common Good
July 2023
519 pages
ISBN:9781450399852
DOI:10.1145/3569951
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2023

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  1. HPC training and education
  2. data driven evaluation
  3. learning analytics
  4. training evaluation

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  • Refereed limited

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PEARC '23
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