Article

Examining the role of self-regulated learning on introductory programming performance

Authors:

Desmond TraynorAuthors Info & Claims

ICER '05: Proceedings of the first international workshop on Computing education research

Pages 81 - 86

https://doi.org/10.1145/1089786.1089794

Published: 01 October 2005 Publication History

Abstract

The purpose of this study was to investigate the relationship between self-regulated learning (SRL) and introductory programming performance. Participants were undergraduate students enrolled in an introductory computer programming module at a third-level (post-high school) institution. The instrument used in this study was designed to assess the motivations and learning strategies (cognitive, metacognitive and resource management strategies) of college students. The data gathered was analyzed to determine if a relationship existed between self-regulation and programming performance and investigate if SRL could be used to predict performance on the module. The study found that students who perform well in programming use more metacognitive and resource management strategies than lower performing students. In addition, students who have high levels of intrinsic motivation and task value perform better in programming and use more metacognitive and resource management strategies than students with low levels of intrinsic motivation and task value. Finally, a regression model based on cognitive, metacognitive and resource management strategies was able to account for 45% of the variance in programming performance results.

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Cited By

Türkmen GHopcan SPolat E(2024)Sustaining Undergraduate Students’ Metacognitive Regulatory Actions During Online Flipped Programming CourseJournal of Learning and Teaching in Digital Age10.53850/joltida.13910399:2(111-128)Online publication date: 10-Jul-2024
https://doi.org/10.53850/joltida.1391039
Margulieux LPrather JReeves BBecker BCetin Uzun GLoksa DLeinonen JDenny PMonga MLonati VBarendsen ESheard JPaterson J(2024)Self-Regulation, Self-Efficacy, and Fear of Failure Interactions with How Novices Use LLMs to Solve Programming ProblemsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653621(276-282)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653621
Hou IMettille SMan OLi ZZastudil CMacNeil S(2024)The Effects of Generative AI on Computing Students’ Help-Seeking PreferencesProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636248(39-48)Online publication date: 29-Jan-2024
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Index Terms

Examining the role of self-regulated learning on introductory programming performance
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

ICER '05: Proceedings of the first international workshop on Computing education research

October 2005

182 pages

ISBN:1595930434

DOI:10.1145/1089786

Program Chairs:
Richard Anderson
University of Washington, Seattle, WA
,
Sally A. Fincher
University of Kent at Canterbury, UK
,
Mark Guzdial
Georgia Institute of Technology, Atlanta, GA

Copyright © 2005 ACM.

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Published: 01 October 2005

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ICER05: International Computing Education Research Workshop 2005

October 1 - 2, 2005

WA, Seattle, USA

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Cited By

Türkmen GHopcan SPolat E(2024)Sustaining Undergraduate Students’ Metacognitive Regulatory Actions During Online Flipped Programming CourseJournal of Learning and Teaching in Digital Age10.53850/joltida.13910399:2(111-128)Online publication date: 10-Jul-2024
https://doi.org/10.53850/joltida.1391039
Margulieux LPrather JReeves BBecker BCetin Uzun GLoksa DLeinonen JDenny PMonga MLonati VBarendsen ESheard JPaterson J(2024)Self-Regulation, Self-Efficacy, and Fear of Failure Interactions with How Novices Use LLMs to Solve Programming ProblemsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653621(276-282)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653621
Hou IMettille SMan OLi ZZastudil CMacNeil S(2024)The Effects of Generative AI on Computing Students’ Help-Seeking PreferencesProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636248(39-48)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636248
Li YChen MHunt AZhang HO'Rourke E(2024)Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-MajorsProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671119(27-41)Online publication date: 12-Aug-2024
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Wortmann CVahrenhold J(2024)Regulation, Self-Efficacy, and Participation in CS1 Group WorkProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671115(359-373)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671115
Cloude EZhang JBaker RFouh EStephenson BStone JBattestilli LRebelsky SShoop L(2024)Procrastination vs. Active Delay: How Students Prepare to Code in Introductory ProgrammingProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630907(214-220)Online publication date: 7-Mar-2024
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Del Carpio Gutierrez ADenny PLuxton-Reilly AStephenson BStone JBattestilli LRebelsky SShoop L(2024)Evaluating Automatically Generated Contextualised Programming ExercisesProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630863(289-295)Online publication date: 7-Mar-2024
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Chiu MMoss ERichards TStephenson BStone JBattestilli LRebelsky SShoop L(2024)Effect of Deadlines on Student Submission Timelines and Success in a Fully-Online Self-Paced CourseProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630837(207-213)Online publication date: 7-Mar-2024
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Prasad PSane AStephenson BStone JBattestilli LRebelsky SShoop L(2024)A Self-Regulated Learning Framework using Generative AI and its Application in CS Educational Intervention DesignProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630828(1070-1076)Online publication date: 7-Mar-2024
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Cheng GZou DXie HWang F(2024)Exploring differences in self-regulated learning strategy use between high- and low-performing students in introductory programmingComputers & Education10.1016/j.compedu.2023.104948208:COnline publication date: 1-Jan-2024
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