skip to main content
10.1145/1089786.1089794acmconferencesArticle/Chapter ViewAbstractPublication PagesicerConference Proceedingsconference-collections
Article

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

Authors Info & Claims
Published:01 October 2005Publication 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.

References

  1. S. Bergin and R. Reilly. The influence of motivation and comfort-level on learning to program. In Proceedings of the 17th Workshop on Psychology of Programming, PPIG'05, 2005.Google ScholarGoogle Scholar
  2. S. Bergin and R. Reilly. Programming: Factors that influence success. In Proceedings of the 36th SIGCSE technical symposium on computer science education, SIGCSE'05, pages 411--415, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. P. F. Campbell and G. P. McCabe. Predicting the success of freshmen in a computer science major. Commun. ACM, 27(11):1108--1113, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. Cantwell-Wilson and S. Shrock. Contributing to success in an introductory computer science course: a study of twelve factors. In Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education, pages 184--188, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. F. Pajares, S. Brinter, and G. Valiante. Relation between achievement goals and self-beliefs of middle school students in writing and science. Contemporary Educational Psychology, 25(4):406--422, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  6. H. Patrick, A. Ryan, and P. Pintrich. The differential impact of extrinsic and mastery goal orientations on males' and females' self-regulating learning. Learning and Individual differences, 11(2):153--171, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  7. P. Pintrich. The dymanic interplay of student motivation and cognition in the college classroom. Advances in motivation and achievement: motivation enhancing environments, 6:117--160, 1989.Google ScholarGoogle Scholar
  8. P. Pintrich. The role of motivation in promoting and sustaining self -regulated learning. International Journal of Educational Research, 31:459--470, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  9. P. Pintrich and E. DeGroot. Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1):33--40, 1990.Google ScholarGoogle ScholarCross RefCross Ref
  10. P. Pintrich and T. Garcia. Student goal orientation and self-regulation in the college classroom. Advances in Motivation and Achievement, 7:371--403, 1991.Google ScholarGoogle Scholar
  11. P. Pintrich and B. Schrauben. 'Students' motivational beliefs and their cognitive engagement in classroom academic tasks' in 'Student perceptions in the classroom'. Hillsdale, NJ: Lawrence Erlbaum, 1992.Google ScholarGoogle Scholar
  12. P. R. Pintrich, D. Smith, T.Garcia, and W. McKeachie. A manual for the use of the motivated strategies for learning questionnaire. technical report 91-b-004. The Regents of the University of Michigan., 1991.Google ScholarGoogle Scholar
  13. P. Pokay and P. Blumenfeld. Predicting achievement early and late in the semester: the role of motivation and learning strategies. Journal of Educational Psychology, 82(1):41--50, 1990.Google ScholarGoogle ScholarCross RefCross Ref
  14. N. Rountree, J. Rountree, and A. Robins. Predictors of success and failure in a cs1 course. SIGCSE Bull., 34(4):121--124, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. B. Zimmerman. Becoming a self-regulated learner: which are the key sub-processes? Contemporary Educational Psychology., 11(4):307--313, 1986.Google ScholarGoogle ScholarCross RefCross Ref
  16. B. Zimmerman and M. Martinez-Pons. Student differences in self-regulated learning: relating grade, sex and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82:51--59, 1990.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

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

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • 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

      Copyright © 2005 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 October 2005

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      Overall Acceptance Rate189of803submissions,24%

      Upcoming Conference

      ICER 2024
      ACM Conference on International Computing Education Research
      August 13 - 15, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader