research-article

Learning from the Impossible: Introducing Theoretical Computer Science in CS Mathematics Courses

Author:

Rafael del Vado VírsedaAuthors Info & Claims

SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

Pages 952 - 958

https://doi.org/10.1145/3408877.3432475

Published: 05 March 2021 Publication History

Abstract

The low academic results and mathematical difficulties experienced by students in learning theoretical computer science have been one of the reasons why many undergraduate students postpone the study of their theoretical subjects until absolutely necessary. For this reason, some optional theoretical subjects have been postponed for the last years, and in some cases, not included in the curricula due to lack of students. In order to reverse this problematic situation in theoretical computing education, it is essential that students acquire as soon as possible an intuitive and progressive knowledge of the main theoretical computing concepts and their associated skills and abilities. To achieve this goal, in this paper we offer a complete educational methodology to systematically introduce questions of computability and algorithmic complexity, in the early years of computer science and mathematics degrees, from the results of impossibility that are already included in the curriculum of CS-geared mathematics courses. To provide clear evidence of the impact of the applicability of the proposed methodology, we analyze the experimental results and student feedback we have obtained to confirm that the introduction of theoretical computing questions from impossibility results increases students' academic results in theoretical computing subjects, and motivation and interest in studying mathematical subjects of beginning level of computer science.

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Cited By

Lee WMoh MKhayrallah RAttar NLam K(2024)WIP: Python for Everyone as a Mathematics GE Course: Broaden Participation and Enhance Data Science Career Pipeline2024 IEEE Frontiers in Education Conference (FIE)10.1109/FIE61694.2024.10892893(1-5)Online publication date: 13-Oct-2024
https://doi.org/10.1109/FIE61694.2024.10892893
Blumenthal RMerkle LDoyle MSheard JSoh LDorn B(2022)Teach More, Not Less Computability Theory in CS202XProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499309(675-681)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499309

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cover image ACM Conferences

SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

March 2021

1454 pages

ISBN:9781450380621

DOI:10.1145/3408877

General Chairs:
Mark Sherriff
University of Virginia, USA
,
Laurence D. Merkle
Air Force Institute of Technology, USA
,
Program Chairs:
Pamela Cutter
Kalamazoo College, USA
,
Alvaro Monge
California State University, Long Beach, USA
,
Judithe Sheard
Monash University, Australia

Copyright © 2021 ACM.

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Published: 05 March 2021

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SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education

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Cited By

Lee WMoh MKhayrallah RAttar NLam K(2024)WIP: Python for Everyone as a Mathematics GE Course: Broaden Participation and Enhance Data Science Career Pipeline2024 IEEE Frontiers in Education Conference (FIE)10.1109/FIE61694.2024.10892893(1-5)Online publication date: 13-Oct-2024
https://doi.org/10.1109/FIE61694.2024.10892893
Blumenthal RMerkle LDoyle MSheard JSoh LDorn B(2022)Teach More, Not Less Computability Theory in CS202XProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499309(675-681)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499309

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