research-article

Modeling how students learn to program

Authors:

Paulo BliksteinAuthors Info & Claims

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

Pages 153 - 160

https://doi.org/10.1145/2157136.2157182

Published: 29 February 2012 Publication History

Abstract

Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.

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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
Wang SMitchell JPiech CStephenson BStone JBattestilli LRebelsky SShoop L(2024)A Large Scale RCT on Effective Error Messages in CS1Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630764(1395-1401)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630764
de Oliveira CSobral SFerreira MMoreira F(2024)Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine LearningProcedia Computer Science10.1016/j.procs.2024.06.212239(598-605)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.212
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Index Terms

Modeling how students learn to program
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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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.

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Published: 29 February 2012

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February 29 - March 3, 2012

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

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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
Wang SMitchell JPiech CStephenson BStone JBattestilli LRebelsky SShoop L(2024)A Large Scale RCT on Effective Error Messages in CS1Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630764(1395-1401)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630764
de Oliveira CSobral SFerreira MMoreira F(2024)Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine LearningProcedia Computer Science10.1016/j.procs.2024.06.212239(598-605)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.212
Kandjimi HSuleman H(2024)Investigating Markov Model Accuracy in Representing Student Programming BehavioursSouth African Computer Science and Information Systems Research Trends10.1007/978-3-031-64881-6_4(62-78)Online publication date: 8-Jul-2024
https://doi.org/10.1007/978-3-031-64881-6_4
Yadav M(2023)Mining student coding behaviors in a programming MOOC: there are no actionable learner stereotypesEducational Technology Quarterly10.55056/etq.611Online publication date: 26-Oct-2023
https://doi.org/10.55056/etq.611
Nguyen BNguyen NChen H(2023)Utilizing Learning Analytics in an Automated Programming Assessment System to Enhance Software Practice EducationPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch017(332-362)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch017
Mansoor NPeterson CDodd MSharif B(2023)Assessing the Effect of Programming Language and Task Type on Eye Movements of Computer Science StudentsACM Transactions on Computing Education10.1145/363253024:1(1-38)Online publication date: 14-Nov-2023
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Kim YPiech CHilliger IKhosravi HRienties BDawson S(2023)The Student Zipf Theory: Inferring Latent Structures in Open-Ended Student Work To Help EducatorsLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576116(464-475)Online publication date: 13-Mar-2023
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Charitsis CPiech CMitchell JDoyle MStephenson BDorn BSoh LBattestilli L(2023)Detecting the Reasons for Program Decomposition in CS1 and Evaluating Their ImpactProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569763(1014-1020)Online publication date: 2-Mar-2023
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