research-article

Detecting and understanding students' misconceptions related to algorithms and data structures

Authors:

Holger Danielsiek,

Jan VahrenholdAuthors Info & Claims

SIGCSE '12: Proceedings of the 43rd ACM technical symposium on Computer Science Education

Pages 21 - 26

https://doi.org/10.1145/2157136.2157148

Published: 29 February 2012 Publication History

Abstract

We describe the first results of our work towards a concept inventory for Algorithms and Data Structures. Based on expert interviews and the analysis of 400 exams we were able to identify several core topics which are prone to error. In a pilot study, we verified misconceptions known from the literature and identified previously unknown misconceptions related to Algorithms and Data Structures. In addition to this, we report on methodological issues and point out the importance of a two-pronged approach to data collection.

References

[1]

V. L. Almstrum, P. B. Henderson, V. J. Harvey, C. Heeren, W. A. Marion, C. Riedesel, L.-K. Soh, and A. E. Tew. Concept inventories in computer science for the topic discrete mathematics. SIGCSE Bulletin, 38(4):132--145, Dec. 2006.

Digital Library

[2]

C. Chang, P. J. Denning, J. J. Cross II, G. Engel, R. Sloan, D. Carver, R. Eckhouse, W. King, F. Lau, S. Mengel, P. Srimani, E. Roberts, R. Shackelford, R. Austing, C. F. Cover, G. Davies, A. McGettrick, G. M. Schneider, and U. Wolz. Computing Curricula 2001: Computer Science. Journal on Educational Resources in Computing, 1, 2001. Article 1, 240 pp.

[3]

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, Cambridge, MA, 2nd ed., 2001.

Digital Library

[4]

A. Eckerdal, R. McCartney, J. E. Moström, M. Ratcliffe, and C. Zander. Can graduating students design software systems? In Proc. 37th SIGCSE Symp. Computer Science Education, pp. 403--407. 2006.

Digital Library

[5]

A. Eckerdal and M. Thuné. Novice Java programmers' conceptions of "object" and "class", and variation theory. In Proc. 10th ITiCSE Conf. Innovation and Technology in Computer Science Education, pp. 89--93. 2005.

Digital Library

[6]

D. Ginat. The baffling CS notions of "as-if" and "don't-care". In Proc. 41st SIGCSE Symp. Computer Science Education, pp. 385--389. 2010.

Digital Library

[7]

K. Goldman, P. Gross, C. Heeren, G. L. Herman, L. C. Kaczmarczyk, M. C. Loui, and C. Zilles. Identifying important and difficult concepts in introductory computing courses using a Delphi process. In Proc. 39th SIGCSE Symp. Computer Science Education, pp. 256--260. 2008.

Digital Library

[8]

G. L. Herman, M. C. Loui, and C. Zilles. Creating the digital logic concept inventory. In Proc. 41th SIGCSE Symp. Computer Science Education, pp. 102--106. 2010.

Digital Library

[9]

S. Holland, R. Griffiths, and M. Woodmann. Avoiding object misconceptions. In Proc. 28th SIGCSE Symp. Computer Science Education, pp. 131--134. 1997.

Digital Library

[10]

L. C. Kaczmarczyk, E. R. Petrick, J. P. East, and G. L. Herman. Identifying student misconceptions of programming. In Proc. 41st SIGCSE Symp. Computer Science Education, pp. 107--111. 2010.

Digital Library

[11]

C. Loftus, L. Thomas, and C. Zander. Can graduating students design: revisited. In Proc. 42nd SIGCSE Symp. Computer Science Education, pp. 105--110. 2011.

Digital Library

[12]

M. McCracken, V. Almstrum, D. Diaz, M. Guzdial, D. Hagan, Y. B.-D. Kolikant, C. Laxer, L. Thomas, I. Utting, and T. Wilusz. A multi-national, multi- institutional study of assessment of programming skills of first-year CS students. SIGCSE Bull., 33:125--180, 2001.

Digital Library

[13]

N. Ragonis and M. Ben-Ari. A long-term investigation of the comprehension of OOP concepts by novices. Computer Science Education, 15(3):203--221, Sept. 2005.

[14]

K. Sanders, J. Boustedt, A. Eckerdal, R. McCartney, J. E. Moström, L. Thomas, and C. Zander. Student understanding of object-oriented programming as expressed in concept maps. In Proc. 39th SIGCSE Symp. Computer Science Education, pp. 332--336. 2008.

Digital Library

[15]

O. Seppala, L. Malmi, and A. Korhonen. Observations on student misconceptions -- a case study of the build-heap algorithm. Computer Science Education, 16(3):241--255. 2006.

[16]

A. E. Tew and M. Guzdial. Developing a validated assessment of fundamental CS1 concepts. In Proc. 41st SIGCSE Symp. Computer Science Education, pp. 97--101. 2010.

Digital Library

Cited By

Tilanterä ASorva JSeppälä OKorhonen A(2024)Students Struggle with Concepts in Dijkstra's AlgorithmProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671096(154-165)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671096
Bellettini CLonati VMonga MMorpurgo AStephenson BStone JBattestilli LRebelsky SShoop L(2024)To Be Or Not To Be . . . An Algorithm: The Notion According to Students and TeachersProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630950(102-108)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630950
Ali MGhosh SRao PDhegaskar RJawort SMedler AShi MDasgupta S(2023)Taking Stock of Concept Inventories in Computing Education: A Systematic Literature ReviewProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600120(397-415)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600120
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Index Terms

Detecting and understanding students' misconceptions related to algorithms and data structures
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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Published In

cover image ACM Conferences

SIGCSE '12: Proceedings of the 43rd ACM technical symposium on Computer Science Education

February 2012

734 pages

ISBN:9781450310987

DOI:10.1145/2157136

Conference Chairs:
Laurie Smith King
College of the Holy Cross
,
David R. Musicant,
Program Chairs:
Tracy Camp
Colorado School of Mines
,
Paul Tymann
Rochester Institute of Technology

Copyright © 2012 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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Published: 29 February 2012

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SIGCSE '12: The 43rd ACM Technical Symposium on Computer Science Education

February 29 - March 3, 2012

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SIGCSE '12 Paper Acceptance Rate 100 of 289 submissions, 35%;

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

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https://dl.acm.org/doi/10.1145/3632620.3671096
Bellettini CLonati VMonga MMorpurgo AStephenson BStone JBattestilli LRebelsky SShoop L(2024)To Be Or Not To Be . . . An Algorithm: The Notion According to Students and TeachersProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630950(102-108)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630950
Ali MGhosh SRao PDhegaskar RJawort SMedler AShi MDasgupta S(2023)Taking Stock of Concept Inventories in Computing Education: A Systematic Literature ReviewProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600120(397-415)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600120
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Prasad SGreenman BNelson TWrenn JKrishnamurthi S(2022)Making Hay from Wheats: A Classsourcing Method to Identify MisconceptionsProceedings of the 22nd Koli Calling International Conference on Computing Education Research10.1145/3564721.3564726(1-7)Online publication date: 17-Nov-2022
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