Data-Driven Discovery of Anchor Points for PDC Content

Matthew McQuaigue; Erik Saule; Kalpathi Subramanian; Jamie Payton

doi:10.1145/3624062.3624099

Data-Driven Discovery of Anchor Points for PDC Content

Matthew McQuaigue
, Erik Saule
, Kalpathi Subramanian
, Jamie Payton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Publisher	Association for Computing Machinery
Pages	335-342
Number of pages	8
ISBN (Electronic)	9798400707858
DOIs	https://doi.org/10.1145/3624062.3624099
State	Published - Nov 12 2023
Externally published	Yes
Event	2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 - Denver, United States Duration: Nov 12 2023 → Nov 17 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Country/Territory	United States
City	Denver
Period	11/12/23 → 11/17/23

All Science Journal Classification (ASJC) codes

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Keywords

Course Model
CS Education
Curriculum Guidelines
Integrating PDC in Early CS

Access to Document

10.1145/3624062.3624099

Cite this

McQuaigue, M., Saule, E., Subramanian, K., & Payton, J. (2023). Data-Driven Discovery of Anchor Points for PDC Content. In Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 (pp. 335-342). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3624062.3624099

McQuaigue, Matthew ; Saule, Erik ; Subramanian, Kalpathi et al. / Data-Driven Discovery of Anchor Points for PDC Content. Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery, 2023. pp. 335-342 (ACM International Conference Proceeding Series).

@inproceedings{4b0fa446a9784983a2c1c819b6e431b6,

title = "Data-Driven Discovery of Anchor Points for PDC Content",

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.",

keywords = "Course Model, CS Education, Curriculum Guidelines, Integrating PDC in Early CS",

author = "Matthew McQuaigue and Erik Saule and Kalpathi Subramanian and Jamie Payton",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 ; Conference date: 12-11-2023 Through 17-11-2023",

year = "2023",

month = nov,

day = "12",

doi = "10.1145/3624062.3624099",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "335--342",

booktitle = "Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023",

}

McQuaigue, M, Saule, E, Subramanian, K & Payton, J 2023, Data-Driven Discovery of Anchor Points for PDC Content. in Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 335-342, 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, United States, 11/12/23. https://doi.org/10.1145/3624062.3624099

Data-Driven Discovery of Anchor Points for PDC Content. / McQuaigue, Matthew; Saule, Erik; Subramanian, Kalpathi et al.
Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery, 2023. p. 335-342 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Data-Driven Discovery of Anchor Points for PDC Content

AU - McQuaigue, Matthew

AU - Saule, Erik

AU - Subramanian, Kalpathi

AU - Payton, Jamie

PY - 2023/11/12

Y1 - 2023/11/12

N2 - 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.

AB - 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.

KW - Course Model

KW - CS Education

KW - Curriculum Guidelines

KW - Integrating PDC in Early CS

UR - https://www.scopus.com/pages/publications/85178098099

UR - https://www.scopus.com/pages/publications/85178098099#tab=citedBy

U2 - 10.1145/3624062.3624099

DO - 10.1145/3624062.3624099

M3 - Conference contribution

AN - SCOPUS:85178098099

T3 - ACM International Conference Proceeding Series

SP - 335

EP - 342

BT - Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023

PB - Association for Computing Machinery

T2 - 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023

Y2 - 12 November 2023 through 17 November 2023

ER -

McQuaigue M, Saule E, Subramanian K, Payton J. Data-Driven Discovery of Anchor Points for PDC Content. In Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023. Association for Computing Machinery. 2023. p. 335-342. (ACM International Conference Proceeding Series). doi: 10.1145/3624062.3624099

Data-Driven Discovery of Anchor Points for PDC Content

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this