research-article

Identifying challenging CS1 concepts in a large problem dataset

Authors:

Yuliya Cherenkova,

Daniel Zingaro,

Andrew PetersenAuthors Info & Claims

SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science education

Pages 695 - 700

https://doi.org/10.1145/2538862.2538966

Published: 05 March 2014 Publication History

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Abstract

We examine student difficulties with CS1 concepts by analyzing a dataset containing 266,852 student responses to weekly code-writing problems. We find that conditionals and loops prove particularly problematic, even when considering 'second chance' data; and that, while we observe some evidence of improvement, certain straightforward applications of loops continue to be problematic at the end of the term. Our contribution is the corroboration of earlier findings, and a call to use online repositories of student submissions as rich sources of data on the student learning experience.

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Poitras EDempsey DCrane BSimpson SSiegel A(2023)The Applications of Learning Analytics to Enhance Learning and Engagement in Introductory Programming InstructionPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch005(89-108)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch005
Cifuentes Osorio YTorres Pardo IGonzález Gil M(2023)Modelos mentales y algoritmos de programación en estudiantes de media técnica en informáticaRevista Virtual Universidad Católica del Norte10.35575/rvucn.n69a5(98-134)Online publication date: 9-May-2023
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Zimmermann RAllin SZhang L(2023)Common Errors in Machine Learning Projects: A Second LookProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631808(1-12)Online publication date: 13-Nov-2023
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Index Terms

Identifying challenging CS1 concepts in a large problem dataset
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science education

March 2014

800 pages

ISBN:9781450326056

DOI:10.1145/2538862

Conference Chairs:
J. D. Dougherty
J.D. Dougherty, Haverford College
,
Kris Nagel
Georgia Gwinnett College
,
Program Chairs:
Adrienne Decker
Rochester Institute of Technology
,
Kurt Eiselt
University of British Columbia

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: 05 March 2014

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SIGCSE '14: The 45th ACM Technical Symposium on Computer Science Education

March 5 - 8, 2014

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SIGCSE '14 Paper Acceptance Rate 108 of 274 submissions, 39%;

Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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https://doi.org/10.4018/978-1-6684-9527-8.ch005
Cifuentes Osorio YTorres Pardo IGonzález Gil M(2023)Modelos mentales y algoritmos de programación en estudiantes de media técnica en informáticaRevista Virtual Universidad Católica del Norte10.35575/rvucn.n69a5(98-134)Online publication date: 9-May-2023
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Zimmermann RAllin SZhang L(2023)Common Errors in Machine Learning Projects: A Second LookProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631808(1-12)Online publication date: 13-Nov-2023
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Identifying student misconceptions of programming

CS0 vs. CS1:: Understanding Fears and Confidence amongst Non-majors in Introductory CS Courses

What do "CS1" and "CS2" mean?: investigating differences in the early courses

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