research-article

Public Access

Developing Course-Level Learning Goals for Basic Data Structures in CS2

Authors:

Daniel Zingaro,

Cynthia Taylor,

Michael ClancyAuthors Info & Claims

SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education

Pages 858 - 863

https://doi.org/10.1145/3159450.3159457

Published: 21 February 2018 Publication History

Abstract

Establishing learning goals for a course allows instructors to design course content to address those goals, helps students to focus their learning appropriately, and enables researchers to assess learning of those goals. In this work, we propose six learning goals for a topic prevalent in CS2 courses: Basic Data Structures. These learning goals arise from reviewing several CS2 courses at a variety of institutions, surveying faculty experts who commonly teach CS2, and meeting and working closely with these experts. We outline our process for creating learning goals, identify important topics underlying these goals, and provide examples of how the goals developed on the path to consensus. We also document that the term "CS2" does not have a unified interpretation within the CS education community and describe how this hurdle influenced our decision to focus on Basic Data Structures.

References

[1]

ACM/IEEE-CS Joint Task Force on Computing Curricula. Computer science curricula 2013. Technical report, ACM Press and IEEE Computer Society Press, December 2013.

[2]

W. Adams and C. Wieman. Development and validation of instruments to measure learning of expert-like thinking. International Journal of Science Education, 33(9):1289--1312, 2011.

[3]

Carl Wieman Science Education Initiative at the University of British Columbia. Computer science learning goals. www.cwsei.ubc.ca/Files/ComSc_LG/CPSC_Learning_Goals.pdf, 2008.

[4]

S. Chasteen, K. Perkins, P. Beale, S. Pollock, and C. Wieman. A thoughtful approach to instruction: Course transformation for the rest of us. Journal of College Science Teaching, 40:24--30, 2011.

[5]

S. V. Chasteen, R. E. Pepper, M. D. Caballero, S. J. Pollock, and K. K. Perkins. Colorado upper-division electrostatics diagnostic: A conceptual assessment for the junior level. Physical Review Special Topics - Physics Education Research, 8(2), 2012.

[6]

H. Danielsiek, W. Paul, and J. Vahrenhold. Detecting and understanding students' misconceptions related to algorithms and data structures. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, pages 21--26, 2012.

Digital Library

[7]

L. Deslauriers, S. E. Harris, E. Lane, and C. E. Wieman. Transforming the lowest-performing students: an intervention that worked. Journal of College Science Teaching, 41:80--88, 2012.

[8]

A. Fekete. Using counter-examples in the data structures course. In Proceedings of the Fifth Australasian Conference on Computing Education - Volume 20, pages 179--186, 2003.

Digital Library

[9]

K. Goldman, P. Gross, C. Heeren, G. Herman, L. Kaczmarczyk, M. C. Loui, and C. Zilles. Identifying important and difficult concepts in introductory computing courses using a delphi process. SIGCSE Bulletin, 40(1):256--260, 2008.

Digital Library

[10]

G. L. Herman and M. C. Loui. Identifying the core conceptual framework of digital logic. In Proceedings of the 2012 American Society for Engineering Education Annual Conference and Exposition, pages AC2012--4637, 2012.

[11]

M. Hertz. What do "CS1" and "CS2" mean?: Investigating differences in the early courses. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, pages 199--203, 2010.

Digital Library

[12]

K. Karpierz and S. A. Wolfman. Misconceptions and concept inventory questions for binary search trees and hash tables. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, pages 109--114, 2014.

Digital Library

[13]

P. Marsh. What is known about student learning outcomes and how does it relate to the scholarship of teaching and learning? International Journal for the Scholarship of Teaching and Learning, 1(2), 2007.

[14]

C. Schulte and J. Bennedsen. What do teachers teach in introductory programming? In Proceedings of the Second International Workshop on Computing Education Research, pages 17--28, 2006.

Digital Library

[15]

B. Simon and J. Taylor. What is the value of course-specific learning goals? Journal of College Science Teaching, 39(2):52--57, 2009.

[16]

A. E. Tew and M. Guzdial. Developing a validated assessment of fundamental CS1 concepts. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, pages 97--101, 2010.

Digital Library

Cited By

Zhang Y(2024)Application of Algorithm Visualization Techniques in Teaching Computer Data Structure CourseApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-07889:1Online publication date: 1-Apr-2024
https://doi.org/10.2478/amns-2024-0788
Chávez V(2023)How My Students and I (Re)Discovered the Joy of Computing in CS2ACM Inroads10.1145/359691814:2(36-39)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3596918
Gaber IKirsh AStatter DLaakso MMonga MSimon Sheard J(2023)Studied Questions in Data Structures and Algorithms AssessmentsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588843(250-256)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588843
Show More Cited By

Index Terms

Developing Course-Level Learning Goals for Basic Data Structures in CS2
1. Social and professional topics
  1. Professional topics
    1. Computing education

Recommendations

What do "CS1" and "CS2" mean?: investigating differences in the early courses
SIGCSE '10: Proceedings of the 41st ACM technical symposium on Computer science education

Thirty-one years ago, the ACM Computing Curricula used the terms "CS1" and "CS2" to designate the first two two courses in the introductory sequence of a computer science major. While computer science education has greatly changed since that time, we ...
Developing goals and objectives for gameplay and learning
Learning, Education and Games

When designing learning games consider how the learning goals can interact with the game goals and how both should be addressed through the game mechanics used in the game.

Let the design of the progress toward the game goals make it necessary to engage ...
Factors Influencing Student Performance and Persistence in CS2
SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1

Performance in CS1 and introductory CS courses has been an area of active research in the CS education research community for more than four decades, but studies related to student performance in CS2 are not as widely available. Past studies have ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education

February 2018

1174 pages

ISBN:9781450351034

DOI:10.1145/3159450

General Chairs:
Tiffany Barnes
North Carolina State University, USA
,
Daniel Garcia
University of California, Berkeley, USA
,
Program Chairs:
Elizabeth K. Hawthorne
Union County College, USA
,
Manuel A. Pérez-Quiñones
UNC Charlotte, USA

Copyright © 2018 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]

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 February 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF

Conference

SIGCSE '18

Sponsor:

SIGCSE

SIGCSE '18: The 49th ACM Technical Symposium on Computer Science Education

February 21 - 24, 2018

Maryland, Baltimore, USA

Acceptance Rates

SIGCSE '18 Paper Acceptance Rate 161 of 459 submissions, 35%;

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

Upcoming Conference

SIGCSE Virtual 2024

Sponsor:
sigcse

1st ACM Virtual Global Computing Education Conference

December 5 - 8, 2024

Virtual Event , NC , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
885
Total Downloads

Downloads (Last 12 months)151
Downloads (Last 6 weeks)20

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang Y(2024)Application of Algorithm Visualization Techniques in Teaching Computer Data Structure CourseApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-07889:1Online publication date: 1-Apr-2024
https://doi.org/10.2478/amns-2024-0788
Chávez V(2023)How My Students and I (Re)Discovered the Joy of Computing in CS2ACM Inroads10.1145/359691814:2(36-39)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3596918
Gaber IKirsh AStatter DLaakso MMonga MSimon Sheard J(2023)Studied Questions in Data Structures and Algorithms AssessmentsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588843(250-256)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588843
Smarr SGilbert J(2023)Experiences Teaching Data Structures at HBCUs (and the Case for Cultural Pedagogy)2023 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)10.1109/RESPECT60069.2023.00023(77-81)Online publication date: 20-Jun-2023
https://doi.org/10.1109/RESPECT60069.2023.00023
Sarsa SDenny PHellas ALeinonen J(2022)Automatic Generation of Programming Exercises and Code Explanations Using Large Language ModelsProceedings of the 2022 ACM Conference on International Computing Education Research - Volume 110.1145/3501385.3543957(27-43)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1145/3501385.3543957
Lin KMerkle LDoyle MSheard JSoh LDorn B(2022)CS Education for the Socially-Just Worlds We NeedProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499291(265-271)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499291
Feijoo-Garcia PKapoor AGardner-McCune CRagan E(2022)Effects of a Block-Based Scaffolded Tool on Students’ Introduction to Hierarchical Data StructuresIEEE Transactions on Education10.1109/TE.2021.310960465:2(191-199)Online publication date: May-2022
https://doi.org/10.1109/TE.2021.3109604
Shindler MPinpin NMarkovic MReiber FKim JCarlos GDogucu MHong MLuu MAnderson BCote AFerland MJain PLaBonte TMathur LMoreno RSakuma R(2022)Student misconceptions of dynamic programming: a replication studyComputer Science Education10.1080/08993408.2022.207986532:3(288-312)Online publication date: 19-Jun-2022
https://doi.org/10.1080/08993408.2022.2079865
Lin WKuo TChen CHuang Y(2022)AR Compiler: A Visualization Data Structured Program Learning SystemInnovative Technologies and Learning10.1007/978-3-031-15273-3_7(63-67)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-15273-3_7
Webb KZingaro DLiao STaylor CLee CClancy MPorter L(2021)Student Performance on the BDSI for Basic Data StructuresACM Transactions on Computing Education10.1145/347065422:1(1-34)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3470654
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents