research-article

Predicting student success using fine grain clicker data

Authors:

Daniel Zingaro,

Raymond ListerAuthors Info & Claims

ICER '14: Proceedings of the tenth annual conference on International computing education research

Pages 51 - 58

https://doi.org/10.1145/2632320.2632354

Published: 28 July 2014 Publication History

Abstract

Recent research suggests that the first weeks of a CS1 course have a strong influence on end-of-course student performance. The present work aims to refine the understanding of this phenomenon by using in-class clicker questions as a source of student performance. Clicker questions generate per-lecture and per-question data with which to assess student understanding. This work demonstrates that clicker question performance early in the term predicts student outcomes at the end of the term. The predictive nature of these questions applies to code-writing questions, multiple choice questions, and the final exam as a whole. The most predictive clicker questions are identified and the relationships between these questions and final exam performance are examined.

References

[1]

A. Ahadi and R. Lister. Geek genes, prior knowledge, stumbling points and learning edge momentum: parts of the one elephant? In ICER, pages 123--128, 2013.

Digital Library

[2]

A. Ahadi, R. Lister, and D. Teague. Falling behind early and staying behind when learning to program. In 25th Anniversary Psychology of Programming Annual Conference, 2014.

[3]

J. Bennedsen and M. E. Caspersen. Failure rates in introductory programming. SIGCSE Bulletin, 39:32--36, 2007.

Digital Library

[4]

R. Duda, J. Gaschnig, and P. Hart. Model design in the PROSPECTOR consultant system for mineral exploration. In Expert systems in the micro- electronic age, pages 153--167. 1979.

[5]

M. A. Hudak and D. E. Anderson. Formal operations and learning style predict success in statistics and computer science courses. Teaching of Psychology, 17(4):231--234, 1990.

[6]

R. Lister, C. Fidge, and D. Teague. Further evidence of a relationship between explaining, tracing and writing skills in introductory programming. In ITiCSE, pages 161--165, 2009.

Digital Library

[7]

L. Porter, C. Bailey-Lee, B. Simon, and D. Zingaro. Peer instruction: Do students really learn from peer discussion in computing? In ICER, 2011.

Digital Library

[8]

L. Porter, C. B. Lee, and B. Simon. Halving fail rates using peer instruction: A study of four computer science courses. In SIGCSE, pages 177--182, 2013.

Digital Library

[9]

L. Porter and B. Simon. Retaining nearly one-third more majors with a trio of instructional best practices in cs1. In SIGCSE, pages 165--170, 2013.

Digital Library

[10]

L. Porter and D. Zingaro. Importance of early performance in cs1: Two conflicting assessment stories. In SIGCSE, pages 295--300, 2014.

Digital Library

[11]

A. Robins. Learning edge momentum: A new account of outcomes. Computer Science Education, 20(1):37--71, 2010.

[12]

B. Simon, M. Kohanfars, J. Lee, K. Tamayo, and Q. Cutts. Experience report: Peer instruction in introductory computing. In SIGCSE, pages 341--345, 2010.

Digital Library

[13]

E. Soloway. Cognitive strategies and looping constructs: An empirical study. Communications of the ACM, 26(11):853--860, 1983.

Digital Library

[14]

A. Venables, G. Tan, and R. Lister. A closer look at tracing, explaining and code writing skills in the novice programmer. In ICER, pages 117--128, 2009.

Digital Library

[15]

D. Wright, K. London, and A. Field. Using bootstrap estimation and the plug-in principle for clinical psychology data. Journal of Experimental Psychopathology, 2(2):252--270, 2011.

[16]

D. Zingaro. Experience report: Peer instruction in remedial computer science. In Ed-Media, pages 5030--5035, 2010.

[17]

D. Zingaro. Peer instruction contributes to self-efficacy in CS1. In SIGCSE, pages 373--378, 2014.

Digital Library

Cited By

Rys-Recker NHao Q(2024)Early Identification of Struggling Students in Large Computer Science Courses: A Replication Study2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00022(88-93)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00022
Quille KVidal-Meliá LNolan KMooney A(2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3633083.3633190
Parker MDavidson MKao YMargulieux LTidler ZVahrenhold J(2023)Toward CS1 Content Subscales: A Mixed-Methods Analysis of an Introductory Computing AssessmentProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631828(1-13)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3631802.3631828
Show More Cited By

Index Terms

Predicting student success using fine grain clicker data
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        Information science education

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Published In

cover image ACM Conferences

ICER '14: Proceedings of the tenth annual conference on International computing education research

July 2014

186 pages

ISBN:9781450327558

DOI:10.1145/2632320

General Chairs:
Quintin Cutts
University of Glasgow, UK
,
Beth Simon
University of California, San Diego, USA
,
Brian Dorn
University of Nebraska, Omaha, USA

Copyright © 2014 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

Publication History

Published: 28 July 2014

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Funding Sources

National Science Foundation

Conference

ICER '14

Sponsor:

SIGCSE

ICER '14: International Computing Education Research Conference

August 11 - 13, 2014

Scotland, Glasgow, United Kingdom

Acceptance Rates

ICER '14 Paper Acceptance Rate 17 of 69 submissions, 25%;

Overall Acceptance Rate 189 of 803 submissions, 24%

Upcoming Conference

ICER 2025

Sponsor:
sigcse

ACM Conference on International Computing Education Research

August 3 - 6, 2025

Charlottesville , VA , USA

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Cited By

Rys-Recker NHao Q(2024)Early Identification of Struggling Students in Large Computer Science Courses: A Replication Study2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00022(88-93)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00022
Quille KVidal-Meliá LNolan KMooney A(2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3633083.3633190
Parker MDavidson MKao YMargulieux LTidler ZVahrenhold J(2023)Toward CS1 Content Subscales: A Mixed-Methods Analysis of an Introductory Computing AssessmentProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631828(1-13)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3631802.3631828
Kaur AChahal K(2023)A learning analytics dashboard for data-driven recommendations on influences of non-cognitive factors in introductory programmingEducation and Information Technologies10.1007/s10639-023-12125-529:8(9221-9256)Online publication date: 7-Sep-2023
https://doi.org/10.1007/s10639-023-12125-5
Kaur AChahal K(2023)A Review on the Impact of Cognitive Factors in Introductory ProgrammingProceedings of Fourth International Conference on Communication, Computing and Electronics Systems10.1007/978-981-19-7753-4_77(1019-1032)Online publication date: 15-Mar-2023
https://doi.org/10.1007/978-981-19-7753-4_77
Leinonen JCastro FHellas A(2022)Time-on-task metrics for predicting performanceACM Inroads10.1145/353456413:2(42-49)Online publication date: 17-May-2022
https://dl.acm.org/doi/10.1145/3534564
Koutcheme CSarsa SHellas AHaaranen LLeinonen J(2022)Methodological Considerations for Predicting At-risk StudentsProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511873(105-113)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1145/3511861.3511873
Shrestha RLeinonen JZavgorodniaia AHellas AEdwards JPéraire CSerebrenik A(2022)Pausing while programmingProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3510456.3514146(187-198)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510456.3514146
Quille KNam Liao SCostelloe ENolan KMooney AShah KBecker BQuille KLaakso MBarendsen ESimon (2022)PreSSProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524755(54-60)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524755
Al Madi NPeng SRogers TMerkle LDoyle MSheard JSoh LDorn B(2022)Assessing Workload Perception in Introductory Computer Science Projects using NASA-TLXProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499406(668-674)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499406
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