skip to main content
10.1145/3624062.3624099acmotherconferencesArticle/Chapter ViewAbstractPublication PagesscConference Proceedingsconference-collections
research-article

Data-Driven Discovery of Anchor Points for PDC Content

Published:12 November 2023Publication History

ABSTRACT

The Parallel and Distributed Computing community has been interested in integrating PDC content into early CS curriculum to prime the students for more advanced materials and build a workforce able to leverage advanced computing infrastructure. To deploy this strategy at scale, it is important to identify anchor points in early CS courses where we can insert PDC content.

We present an analysis of CS courses that primarily focuses on CS1 and Data Structure courses. We collected data on course content through in-person workshops, where instructors of courses classified their course materials against standard curriculum guidelines.

By using these classification, we make sense of how Computer Science is being taught. We highlight different types of CS1 and Data Structure courses. And we provide reflection on how that knowledge can be used by PDC experts to identify anchoring points for PDC content, while being sensitive to the needs of instructors.

References

  1. 2005. Modern Multidimensional Scaling: Theory and Applications (2nd ed.). Springer-Verlag New York. https://doi.org/10.1007/0-387-28981-XGoogle ScholarGoogle ScholarCross RefCross Ref
  2. ACM Data Science Task Force. 2019. Computing Competencies for Undergraduate Data Science Curricula (Draft). Technical Report. ACM. available at http://www.cs.williams.edu/ andrea/DSTF/index.html.Google ScholarGoogle Scholar
  3. National Security Agency. 2018. Centers of Academic Excellence in Cyber Defense (CAE-CD) – 2019 Knowledge Units. Technical Report. NSA.Google ScholarGoogle Scholar
  4. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. J. Mach. Learn. Res. 3 (March 2003), 993–1022.Google ScholarGoogle Scholar
  5. B. S. Bloom, M. D. Engelhart, E. J. furst, W. H. Hill, and D. R. Krathwohl. 1956. Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain.David McKay Company.Google ScholarGoogle Scholar
  6. College Board. Fall 2014. Computer Science A: Course Description. College Board AP. https://apcentral.collegeboard.org/pdf/ap-computer-science-a-course-description.pdfGoogle ScholarGoogle Scholar
  7. College Board. Fall 2017. AP Computer Science Principles, Including the Curriculum Framework. College Board.Google ScholarGoogle Scholar
  8. EduHPC. 2018. Peachy Parallel Assignments. http://tcpp.cs.gsu.edu/curriculum/?q=peachyGoogle ScholarGoogle Scholar
  9. Alec Goncharow, Matthew Mcquaigue, Erik Saule, Kalpathi Subramanian, Paula Goolkasian, and Jamie Payton. 2021. CS-Materials: A System for Classifying and Analyzing Pedagogical Materials to Improve Adoption of Parallel and Distributed Computing Topics in Early CS Courses. J. Parallel and Distrib. Comput. 157 (2021), 316–330. https://doi.org/10.1016/j.jpdc.2021.05.014Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Alec Goncharow, Matthew Mcquaigue, Erik Saule, Kalpathi Subramanian, Jamie Payton, and Paula Goolkasian. 2021. Mapping Materials to Curriculum Standards for Design, Alignment, Audit, and Search. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (Virtual Event, USA) (SIGCSE ’21). Association for Computing Machinery, New York, NY, USA, 295–301. https://doi.org/10.1145/3408877.3432388Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Joint Taskforce on ACM Curricula. 2013. Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. ACM/IEEE Computer Society. https://www.acm.org/binaries/content/assets/education/cs2013_web_final.pdfGoogle ScholarGoogle Scholar
  12. Daniel Lee and H. Sebastian Seung. 2000. Algorithms for Non-negative Matrix Factorization. In Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp (Eds.). Vol. 13. MIT Press.Google ScholarGoogle Scholar
  13. S.J. Matthews. 2023. PDC Unplugged. http://www.pdcunplugged.org/. Accessed, July 2023.Google ScholarGoogle Scholar
  14. NSF/IEEE-TCPP Curriculum Working Group. 2012. NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing : Core Topics for Undergraduates. Technical Report. CDER. available at http://www.cs.gsu.edu/ tcpp/curriculum/sites/default/files/NSF-TCPP-curriculum-version1.pdf.Google ScholarGoogle Scholar
  15. NSF/IEEE-TCPP Curriculum Working Group. 2020. NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing : Core Topics for Undergraduates (Version 2.0-beta). Technical Report. CDER. available at https://tcpp.cs.gsu.edu/curriculum/?q=system/files/TCPP%20PDC%20Curriculum%20V2.0beta-Nov12.2020.pdf.Google ScholarGoogle Scholar
  16. Nick Parlante. 2018. Nifty Assignments. http://nifty.stanford.edu/Google ScholarGoogle Scholar
  17. Erik Saule. 2023. CS Materials. https://cs-materials.herokuapp.com/.Google ScholarGoogle Scholar
  18. The Joint Taskforce on Computing Curricula: Association for Computing Machinery (ACM), IEEE Computer Society, AAAI. 2023. CS2023: ACM/IEEE-CS/AAAI Computer Science Curricula. ACM/IEEE Computer Society/AAAI. https://csed.acm.org/wp-content/uploads/2023/03/Version-Beta-v2.pdf.Google ScholarGoogle Scholar

Index Terms

  1. Data-Driven Discovery of Anchor Points for PDC Content
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
          November 2023
          2180 pages
          ISBN:9798400707858
          DOI:10.1145/3624062

          Copyright © 2023 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 November 2023

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited
        • Article Metrics

          • Downloads (Last 12 months)10
          • Downloads (Last 6 weeks)2

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format