research-article

Using CodeBrowser to seek differences between novice programmers

Authors:

Kenny Heinonen,

Kasper Hirvikoski,

Matti Luukkainen,

Arto VihavainenAuthors Info & Claims

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

Pages 229 - 234

https://doi.org/10.1145/2538862.2538981

Published: 05 March 2014 Publication History

Abstract

A large body of systems that gather data on students' programming process exists, and with the increase of massive open online courses in programming, the amount of gathered data is growing even at a higher rate. A common issue for data analysis is the lack of common tools for visualizing source code snapshots. We have created a browser-side snapshot analysis tool called CodeBrowser that provides a clean REST API that anyone can integrate their snapshot data into. In this article, we describe CodeBrowser and as an example, discuss how it has been used to seek differences between novice programmers that have passed (n=10) or failed (n=10) an introductory programming course.

References

[1]

K. M. Ala-Mutka. A Survey of Automated Assessment Approaches for Programming Assignments. Computer Science Education, 15:83--102, June 2005.

[2]

E. Balzuweit and J. Spacco. SnapViz: visualizing programming assignment snapshots. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education, ITiCSE '13, pages 350--350, New York, NY, USA, 2013. ACM.

Digital Library

[3]

P. Blikstein. Using learning analytics to assess students' behavior in open-ended programming tasks. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK '11, pages 110--116, New York, NY, USA, 2011. ACM.

Digital Library

[4]

C. Douce, D. Livingstone, and J. Orwell. Automatic test-based assessment of programming: A review. J. Educ. Resour. Comput., 5(3), Sept. 2005.

Digital Library

[5]

P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppala. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli Calling '10, pages 86--93, New York, NY, USA, 2010. ACM.

Digital Library

[6]

M. C. Jadud. Methods and tools for exploring novice compilation behaviour. In Proceedings of the second international workshop on Computing education research, ICER '06, pages 73--84, New York, NY, USA, 2006. ACM.

Digital Library

[7]

V. Karavirta, A. Korhonen, L. Malmi, and T. Naps. A comprehensive taxonomy of algorithm animation languages. J. Vis. Lang. Comput., 21(1):1--22, Feb. 2010.

Digital Library

[8]

M. Kölling, B. Quig, A. Patterson, and J. Rosenberg. The BlueJ system and its pedagogy. Computer Science Education, 13(4):249--268, 2003.

[9]

Y. Matsuzawa, K. Okada, and S. Sakai. Programming process visualizer: a proposal of the tool for students to observe their programming process. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education, ITiCSE '13, pages 46--51, New York, NY, USA, 2013. ACM.

Digital Library

[10]

J. McKeogh and C. Exton. Eclipse plug-in to monitor the programmer behaviour. In Proceedings of the 2004 OOPSLA workshop on eclipse technology eXchange, eclipse '04, pages 93--97, New York, NY, USA, 2004. ACM.

Digital Library

[11]

A. Moreno, N. Myller, E. Sutinen, and M. Ben-Ari. Visualizing programs with Jeliot 3. In Proceedings of the working conference on Advanced visual interfaces, AVI '04, pages 373--376, New York, NY, USA, 2004. ACM.

Digital Library

[12]

C. Murphy, G. Kaiser, K. Loveland, and S. Hasan. Retina: helping students and instructors based on observed programming activities. In Proceedings of the 40th ACM technical symposium on Computer science education, SIGCSE '09, pages 178--182, New York, NY, USA, 2009. ACM.

Digital Library

[13]

C. Norris, F. Barry, J. B. Fenwick Jr., K. Reid, and J. Rountree. ClockIt: collecting quantitative data on how beginning software developers really work. SIGCSE Bull., 40(3):37--41, June 2008.

Digital Library

[14]

J. Spacco, J. Strecker, D. Hovemeyer, and W. Pugh. Software repository mining with Marmoset: an automated programming project snapshot and testing system. In Proceedings of the 2005 international workshop on Mining software repositories, MSR '05, pages 1--5, New York, NY, USA, 2005. ACM.

Digital Library

[15]

I. Utting, N. Brown, M. Kölling, D. McCall, and P. Stevens. Web-scale data gathering with BlueJ. In Proceedings of the ninth annual international conference on International computing education research, ICER '12, pages 1--4, New York, NY, USA, 2012. ACM.

Digital Library

[16]

A. Vihavainen, T. Vikberg, M. Luukkainen, and M. Partel. Scaffolding students' learning using Test My Code. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education, ITiCSE '13, pages 117--122, New York, NY, USA, 2013. ACM.

Digital Library

[17]

M. R. A. Wittmann, M. Bower, and M. Kavakli-Thorne. Using the SCORE software package to analyse novice computer graphics programming. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education, ITiCSE '11, pages 118--122, New York, NY, USA, 2011. ACM.

Digital Library

Cited By

Zhong HNiu JLi J(2024)InsProg: Supporting Teaching Through Visual Analysis of Students’ Programming ProcessesProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656668(1-5)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656668
Sheard JDenny PHellas ALeinonen JMalmi LSimon Stephenson BStone JBattestilli LRebelsky SShoop L(2024)Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview StudyProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630880(1223-1229)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630880
Lehtinen THaaranen LLeinonen J(2023)Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming ProcessesProceedings of the 25th Australasian Computing Education Conference10.1145/3576123.3576129(49-58)Online publication date: 30-Jan-2023
https://dl.acm.org/doi/10.1145/3576123.3576129
Show More Cited By

Index Terms

Using CodeBrowser to seek differences between novice programmers
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

Recommendations

Why Novice Programmers Fall into a Pitfall?: Coding Pattern Analysis in Programming Exercise
IWESEP '12: Proceedings of the 2012 Fourth International Workshop on Empirical Software Engineering in Practice

It is crucial for educators to understand pitfalls for novice programmers in programming education at computer science course. By giving well-timed advice to students who fall into a pitfall, educators are able to achieve efficient teaching and keep up ...
Program visualization and explanation for novice C programmers
ACE '14: Proceedings of the Sixteenth Australasian Computing Education Conference - Volume 148

Program visualization and natural language explanations of program behaviour have been shown to assist novice programmers with improving their programming knowledge, correcting misunderstandings, and debugging programs. These techniques have been used ...
Teaching Programming Strategies Explicitly to Novice Programmers: Can the way we teach strategies improve novice outcomes?

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

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

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]

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGCSE '14

Sponsor:

SIGCSE

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

March 5 - 8, 2014

Georgia, Atlanta, USA

Acceptance Rates

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

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

Upcoming Conference

SIGCSE TS 2025

Sponsor:
sigcse

The 56th ACM Technical Symposium on Computer Science Education

February 26 - March 1, 2025

Pittsburgh , PA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
372
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhong HNiu JLi J(2024)InsProg: Supporting Teaching Through Visual Analysis of Students’ Programming ProcessesProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656668(1-5)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656668
Sheard JDenny PHellas ALeinonen JMalmi LSimon Stephenson BStone JBattestilli LRebelsky SShoop L(2024)Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview StudyProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630880(1223-1229)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630880
Lehtinen THaaranen LLeinonen J(2023)Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming ProcessesProceedings of the 25th Australasian Computing Education Conference10.1145/3576123.3576129(49-58)Online publication date: 30-Jan-2023
https://dl.acm.org/doi/10.1145/3576123.3576129
Koutcheme CSarsa SLeinonen JHaaranen LHellas A(2023)Evaluating Distance Measures for Program RepairProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600130(495-507)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600130
Scott SHellas ALeinonen JEdwards JHaiduc SSchneider J(2023)Factors Affecting Compilable State at Each Keystroke in CS1Proceedings of the 45th International Conference on Software Engineering: Software Engineering Education and Training10.1109/ICSE-SEET58685.2023.00036(314-323)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1109/ICSE-SEET58685.2023.00036
Meier HLepp M(2023)Clusters of Solvers' Behavioral Patterns Based on Analysis of the Programming Process2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343479(1-6)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343479
Omer UTehseen RFarooq MAbid A(2023)Learning analytics in programming courses: Review and implicationsEducation and Information Technologies10.1007/s10639-023-11611-028:9(11221-11268)Online publication date: 16-Feb-2023
https://doi.org/10.1007/s10639-023-11611-0
Shrestha RLeinonen JHellas AIhantola PEdwards J(2022)CodeProcess Charts: Visualizing the Process of Writing CodeProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511867(46-55)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1145/3511861.3511867
Koivisto THellas A(2022)Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962751(1-9)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962751
Fonseca NMacedo LMendes A(2021)The importance of using the CodeInsights monitoring tool to support teaching programming in the context of a pandemic2021 IEEE Frontiers in Education Conference (FIE)10.1109/FIE49875.2021.9637292(1-8)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1109/FIE49875.2021.9637292
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten