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The Relationship between Prerequisite Proficiency and Student Performance in an Upper-Division Computing Course

Authors:

Sander Valstar,

William G. Griswold,

Leo PorterAuthors Info & Claims

SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education

Pages 794 - 800

https://doi.org/10.1145/3287324.3287419

Published: 22 February 2019 Publication History

Abstract

While it is widely believed that taking a class's prerequisites is critical for success, less is known about how proficiency with the prerequisite knowledge from those courses affects performance in later courses. Specifically, it is unclear how well students understand material from prerequisite courses and whether that understanding may impact their outcomes in the subsequent course. Additionally, in subsequent courses, do students strengthen their knowledge from prerequisite courses and, if they do, does that improvement matter for the subsequent course? This study examines the prerequisite knowledge of 208 students in an upper-division data structures class at a large North American research university. Prerequisite proficiency on entry to the course was surprisingly low, with nearly a third of students demonstrating low proficiency and only a quarter high proficiency. Students modestly improved their proficiency during the term, lifting a third of those with low proficiency to at least medium proficiency. Overall, final exam performance was significantly correlated with prerequisite knowledge. For those with low initial proficiency, improvement in proficiency was significantly correlated with performance on the final. These results suggest that more attention needs to be placed on reinforcing prerequisite knowledge for those with low proficiency.

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Cited By

Yoo JBalachandranath PSaboury S(2024)Prerequisite knowledge‐based automated course planning with semantics considerationComputer Applications in Engineering Education10.1002/cae.2274832:4Online publication date: 24-Apr-2024
https://doi.org/10.1002/cae.22748
Björn CKann VLaakso MMonga MSimon Sheard J(2023)Variables Affecting Students' Success in CS2Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588856(257-263)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588856
Krause-Levy SSalguero ALim RMcTavish HTrajkovic JPorter LGriswold WDoyle MStephenson BDorn BSoh LBattestilli L(2023)Instructor Perspectives on Prerequisite Courses in ComputingProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569787(277-283)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569787
Show More Cited By

Index Terms

The Relationship between Prerequisite Proficiency and Student Performance in an Upper-Division Computing Course
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
      2. Student assessment

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cover image ACM Conferences

SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education

February 2019

1364 pages

ISBN:9781450358903

DOI:10.1145/3287324

General Chairs:
Elizabeth K. Hawthorne
Union County College, USA
,
Manuel A. Pérez-Quiñones
University of North Carolina at Charlotte, USA
,
Program Chairs:
Sarah Heckman
North Carolina State University, USA
,
Jian Zhang
Texas Woman's University, USA

Copyright © 2019 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 ACM 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]

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

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Published: 22 February 2019

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SIGCSE '19

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SIGCSE

SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education

February 27 - March 2, 2019

MN, Minneapolis, USA

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SIGCSE '19 Paper Acceptance Rate 169 of 526 submissions, 32%;

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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SIGCSE TS 2025

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The 56th ACM Technical Symposium on Computer Science Education

February 26 - March 1, 2025

Pittsburgh , PA , USA

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Cited By

Yoo JBalachandranath PSaboury S(2024)Prerequisite knowledge‐based automated course planning with semantics considerationComputer Applications in Engineering Education10.1002/cae.2274832:4Online publication date: 24-Apr-2024
https://doi.org/10.1002/cae.22748
Björn CKann VLaakso MMonga MSimon Sheard J(2023)Variables Affecting Students' Success in CS2Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588856(257-263)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588856
Krause-Levy SSalguero ALim RMcTavish HTrajkovic JPorter LGriswold WDoyle MStephenson BDorn BSoh LBattestilli L(2023)Instructor Perspectives on Prerequisite Courses in ComputingProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569787(277-283)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569787
Chen HWest MHilgenfeldt SSilva MDoyle MStephenson BDorn BSoh LBattestilli L(2023)Measuring the Impact of a Computational Linear Algebra Course on Students' Exam Performance in a Subsequent Numerical Methods CourseProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569778(115-121)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569778
Krause-Levy SValstar SPorter LGriswold W(2022)A demographic analysis on prerequisite preparation in an advanced data structures courseACM Inroads10.1145/353456313:2(34-41)Online publication date: 17-May-2022
https://dl.acm.org/doi/10.1145/3534563
Strömbäck FMannila LKamkar M(2022)Pilot Study of Progvis: A Visualization Tool for Object Graphs and Concurrency via Shared MemoryProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511885(123-132)Online publication date: 14-Feb-2022
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Krause-Levy SLim RVillegas Molina ICao YPorter LBecker BQuille KLaakso MBarendsen ESimon (2022)An Exploration of Student-Tutor Interactions in ComputingProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524786(435-441)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524786
Krause-Levy SValstar SPorter LGriswold WMerkle LDoyle MSheard JSoh LDorn B(2022)A Demographic Analysis on Prerequisite Preparation in an Advanced Data Structures CourseProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499337(661-667)Online publication date: 22-Feb-2022
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Laddha MNavandar SNetak L(2022)Validating Pre-requisite Dependencies Through Student Response AnalysisIntelligent Human Computer Interaction10.1007/978-3-030-98404-5_21(218-228)Online publication date: 20-Mar-2022
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Salguero AGriswold WAlvarado CPorter L(2021)Understanding Sources of Student Struggle in Early Computer Science CoursesProceedings of the 17th ACM Conference on International Computing Education Research10.1145/3446871.3469755(319-333)Online publication date: 16-Aug-2021
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