Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Relationship Between Implicit Intelligence Beliefs and Maladaptive Self-Regulation of Learning

Published: 20 June 2023 Publication History
  • Get Citation Alerts
  • Abstract

    Objectives. Although prior research has uncovered shifts in computer science (CS) students’ implicit beliefs about the nature of their intelligence across time, little research has investigated the factors contributing to these changes. To address this gap, two studies were conducted in which the relationship between ineffective self-regulation of learning experiences and CS students’ implicit intelligence beliefs at different times during the semester was assessed.
    Participants. Participants for Studies 1 (n = 536) and 2 (n = 222) were undergraduate students enrolled in introductory- and upper-level CS courses at a large, public, Midwestern university. Race-ethnicity information was not collected due to IRB concerns about possible secondary identification of participants from underrepresented groups.
    Study Method. Participants completed a condensed version of the Implicit Theories of Intelligence Scale [16, 54] and the Lack of Regulation Scale from the Student Perceptions of Classroom Knowledge Building scale [51, 53] at the beginning (Studies 1 and 2), middle (Study 2), and end (Studies 1 and 2) of semester-long undergraduate CS courses. Survey responses were analyzed using path analyses to investigate how students’ lack of regulation experiences throughout the semester predicted their implicit intelligence beliefs at the beginning (Study 2) and end (Studies 1 and 2) of the semester.
    Findings. Results from Study 1 indicate that undergraduate CS students come to more strongly believe that their intelligence is a fixed, unchanging entity from the beginning until the end of the semester. Moreover, participants’ responses to the lack of regulation scale were predictive of their implicit intelligence beliefs at the end of the semester. Results from Study 2 indicate that ineffective self-regulation experiences early in the semester enhance CS students’ belief in the unchanging nature of intelligence (i.e., during the first half of the semester). Taken altogether, these findings provide evidence that self-regulation experiences influence students’ beliefs about the malleability of intelligence.
    Conclusions. Findings align with Bandura's [4] contention that students’ behaviors and experiences influence their values and beliefs. Students who experienced poor self-regulated learning came to view intelligence as more of a fixed, unalterable entity than their more successfully self-regulated peers. Findings suggest that CS instructors can positively affect student motivation and engagement by embedding self-regulated learning strategy instruction into their courses and helping CS students adopt an incremental-oriented (e.g., growth-oriented) belief system about their intellectual abilities.

    References

    [1]
    Patricia A. Alexander, Diane L. Schallert, and Ralph E. Reynolds. 2009. What is learning anyway? A topographical perspective considered. Educational Psychologist 44, 3 (2009), 176–192. DOI:
    [2]
    Roger Azevedo and Jennifer G. Cromley. 2004. Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of Educational Psychology 96, 3 (2004), 523–535. DOI:
    [3]
    Roger Azevedo, Daniel C. Moos, Jeffrey A. Greene, Fielding I. Winters, and Jennifer G. Cromley. 2008. Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development 56, 1 (2008), 45–72. DOI:
    [4]
    Albert Bandura. 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Inc. New Jersey, USA.
    [5]
    Michael M. Barger and Lisa Linnenbrink-Garcia. 2017. Developmental systems of students' personal theories about education. Educational Psychologist 52, 2 (2017), 63–83. DOI:
    [6]
    Lisa S. Blackwell, Kali H. Trzesniewski, and Carol S. Dweck. 2007. Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development 78, 1 (2007), 246–263. DOI:
    [7]
    Einar Breivik and Ulf H. Olsson. 2001. Adding variables to improve fit: The effect of model size on fit assessment in LISREL. In Structural Equation Modeling: Present and future. A Festschrift in honor of Karl Joreskog. Scientific Software International. Illinois, USA, 169–194.
    [8]
    Jeni L. Burnette, Ernest H. O'Boyle, Eric M. VanEpps, Jeffrey M. Pollack, and Eli J. Finkel. 2013. Met-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin 139, 3 (2013), 655–701. DOI:
    [9]
    Marisa Cohen. 2012. The importance of self-regulation for college student learning. College Student Journal 46, 4 (2012). 892–902.
    [10]
    Ana Costa and Luisa Faria. 2018. Implicit theories of intelligence and academic achievement: A meta-analytic review. Frontiers in Psychology 9 (2018), 1–16. DOI:
    [11]
    Martin V. Covington. 2004. Self-worth theory goes to college or do our motivation theories motivate. In Dennis M. McInerney and Shawn Van Etten (Eds.). Big Theories Revisited. Information Age Publishing, Charlotte, NC. 91–114.
    [12]
    Martin V. Covington. 2009. Self-worth theory: Reflections and prospects. In Kathryn R. Wentzel, Allan Wigfield, and David Miele (Eds.). Handbook of Motivation at School. Taylor & Francis, Milton Park, UK. 141–169.
    [13]
    Quintin Cutts, Emily Cutts, Stephen Draper, Patrick O'Donnell, and Peter Saffrey. 2010. Manipulating mindset to positively influence introductory programming performance. Proceedings of the 41st ACM Technical Symposium on Computer Science Education. Milwaukee, WI, 10-13 March. 431–435, SIGCSE. DOI:
    [14]
    Ting Dai and Jennifer G. Cromley. 2014. Changes in implicit theories of ability in biology and dropout from STEM majors: A latent growth curve approach. Contemporary Educational Psychology 39, 3 (2014), 233–247. DOI:
    [15]
    Caroline Dupeyrat and Claudette Mariné. 2005. Implicit theories of intelligence, goal orientation, cognitive engagement, and achievement: A test of Dweck's model with returning to school adults. Contemporary Educational Psychology 30, 1 (2005), 43–59. DOI:
    [16]
    Carol S. Dweck. 2000. Self-theories: Their Role in Motivation, Personality, and Development. Psychology Press. London, England.
    [17]
    C. S. Dweck. 2007. Mindset: The New Psychology of Success. Ballantine. New York, USA.
    [18]
    Carol S. Dweck and Ellen L. Leggett. 1988. A social-cognitive approach to motivation and personality. Psychological Review 95, 2 (1988), 256–273. DOI:
    [19]
    Carol S. Dweck and Daniel C. Molden. 2005. Self-theories: Their impact on competence motivation and acquisition. In Andrew J. Elliot and Carol S. Dweck (Eds.). Handbook of Competence and Motivation. Guilford Press, New York, NY. 122–140.
    [20]
    Carol S. Dweck and David S. Yeager. 2019. Mindsets: A view from two eras. Perspectives on Psychological Science 14, 3 (2019), 481–496. DOI:
    [21]
    Nickolas Falkner, Rebecca Vivian, David Piper, and Katrina Falkner. 2014. Increasing the effectiveness of automated assessment by increasing marking granularity and feedback units. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, New York, NY, 9–14. DOI:
    [22]
    Abraham E. Flanigan, Markeya S. Peteranetz, Duane F. Shell, and Leen-Kiat Soh. 2017. Implicit intelligence beliefs of computer science students: Exploring change across the semester. Contemporary Educational Psychology 48 (2017), 179–196. DOI:
    [23]
    Abraham E. Flanigan, Markeya S. Peteranetz, Duane F. Shell, and Leen-Kiat Soh. 2022. Shifting beliefs in computer science. ACM Transactions on Computing Education 22, 2 (2022), 1–24. DOI:
    [24]
    Michail N. Giannakos, Ilias O. Pappas, Letizia Jaccheri, and Demetrios G. Sampson. 2017. Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness. Education and Information Technologies 22, 5 (2017), 2365–2382.
    [25]
    Katarzyna Gogol, Martin Brunner, Thomas Goetz, Romain Martin, Sonja Ugen, Ulrich Keller, Antoine Fishbach, and Franzis Preckel. 2014. “My questionnaire is too long!” The assessments of motivational-affective constructs with three-item and single-item measures 39, 3 (2014), 188–205. DOI:
    [26]
    Jamie Gorson and Eleanor O'Rourke. 2019. How do students talk about intelligence? An investigation of motivation, self-efficacy, and mindsets in computer science. In Proceedings of the 2019 ACM Conference on International Computing Education Research (ICER’19). ACM, 21–29.
    [27]
    Heidi Grant and Carol S. Dweck. 2003. Clarifying achievement goals and their impact. Journal of Personality and Social Psychology 85, 3 (2003), 541–553. DOI:
    [28]
    Jeffrey A. Greene, Lara-Jeane Costa, Jane Robertson, Yi Pan, and Victor M. Deekens. 2010. Exploring relations among college students’ prior knowledge, implicit theories of intelligence, and self-regulated learning in a hypermedia environment. Computers & Education 55, 3 (2010), 1027–1043. DOI:
    [29]
    Barbara K. Hofer, Shirley L. Yu, and Paul R. Pintrich. 1998. Teaching college students to be self-regulated learners. In Self-Regulated Learning: From Teaching to Self-Reflective Practice. Guilford Press. New York, USA, 57–85.
    [30]
    Li-tze Hu and Peter M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1 (1999), 1–55.
    [31]
    Clayton Lewis. 2007. Attitudes and beliefs about computer science among students and faculty. ACM SIGCSE Bulletin 39, 2 (2007), 37–41.
    [32]
    Lisa S. Lewis, Cheryl A. Williams, and Stephanie D. Dawson. 2020. Growth mindset training and effective learning strategies in community college registered nursing students. Teaching and Learning in Nursing 15, 2 (2020), 123–127. DOI:
    [33]
    Alex Lishinski, Aman Yadav, Jon Good, and Richard Enbody. 2016. Learning to program: Gender differences and interactive effects of students’ motivation, goals, and self-efficacy on performance. In Proceedings of the 2016 ACM Conference on International Computing Education Research. Association for Computing Machinery, New York, NY, 211–220. DOI:
    [34]
    Dastyni Loksa, Lauren Margulieux, Brett A. Becker, Michelle Craig, Paul Denny, Raymond Pettit, and James Prather. 2022. Metacognition and self-regulation in programming education: Theories and exemplars of use. ACM Transactions on Computing Education 22, 4 (2022), Article 39. DOI:
    [35]
    Carolina Mega, Lucia Ronconi, and Rossana De Beni. 2014. What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology 106, 1 (2014), 121–131. DOI:
    [36]
    Laurie Murphy and Lynda Thomas. 2008. Dangers of a fixed mindset: Implications of self-theories research for computer science education. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education. New York, NY, USA, 271–275. DOI:
    [37]
    Katherine G. Nelson, Duane F. Shell, Jenefer Husman, Evan J. Fishman, and Leen-Kiat Soh. 2015. Motivational and self-regulated learning profiles of students taking a foundational engineering course. Journal of Engineering Education 104, 1 (2015), 74–100. DOI:
    [38]
    Jennifer Parham, Leo Gugerty, and D. E. Stevenson. 2010. Empirical evidence for the existence and uses of metacognition in computer science problem solving. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, New York, NY, 416–420. DOI:
    [39]
    David Paunesku, Gregory M. Walton, Carissa Romero, Eric N. Smith, David S. Yeager, and Carol S. Dweck. 2015. Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science 26, 6 (2015), 784–793. DOI:
    [40]
    Reinhard Pekrun, Thomas Goetz, Wolfram Titz, and Raymond P. Perry. 2002. Positive emotions in education. In Beyond Coping: Meeting Goals, Visions, and Challenges. Oxford, England. Oxford University Press, 149–173.
    [41]
    Paul R. Pintrich and Elisabeth V. De Groot. 1990. Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology 82, 1 (1990), 33–40. DOI:
    [42]
    Paul R. Pintrich, David A. F. Smith, Teresa Garcia, and Wilbert J. McKeachie. 1993. Predictive validity and reliability of the Motivated Strategies for Learning Questionnaire (MSLQ’93). Educational and Psychological Measurement 53 (1993), 801–813.
    [43]
    James Prather, Brett A. Becker, Michelle Craig, Paul Denny, Dastyni Loksa, and Lauren Margulieux. 2020. What do we think we are doing? Metacognition and self-regulation in programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research (ICER’20). ACM, 2–13. DOI:
    [44]
    James Prather, Raymond Pettit, Brett A. Becker, Paul Denny, Dastyni Loksa, Alani Peters, Zachary Albrecht, and Krista Masci. 2019. First things first: Providing metacognitive scaffolding for interpreting problem prompts. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. Association for Computing Machinery. New York, NY, 531–537. DOI:
    [45]
    Michael Pressley, John G. Borkowski, and Wolfgang Schneider. 1989. Good information processing: What it is and how education can promote it. International Journal of Educational Research 13, 8 (1989), 857–867. DOI:
    [46]
    Aneeta Rattan, Catherine Good, and Carol S. Dweck. 2012. “It's ok—Not everyone can be good at math”: Instructors with an entity theory comfort (and demotivate) students. Journal of Experimental Social Psychology 48, 3 (2012), 731–737. DOI:
    [47]
    Kenneth J. Reid and Daniel M. Ferguson. 2014. Assessing changes in mindset of freshman engineers. In Proceedings of the 2014 ASEE North Central Section Conference (Rochester, Michigan, April 04-05, 2014). NCS’14.
    [48]
    Richard W. Robins and Jennifer L. Pals. 2002. Implicit self-theories in the academic domain: Implications for goal orientation, attributions, affect, and self-esteem change. Self and Identity 78, 1 (2002), 246–263. DOI:
    [49]
    Dale H. Schunk. 1995. Self-efficacy, motivation, and performance. Journal of Applied Sport Psychology 7, 2 (1995), 112–137. DOI:
    [50]
    Duane F. Shell, David W. Brooks, Guy Trainin, Kathleen M. Wilson, Douglas F. Kaufmann, and Lynne M. Herr. 2010. The Unified Learning Model. Springer. New York, USA.
    [51]
    Duane F. Shell and Jenefer Husman. 2008. Control, motivation, affect, and strategic self-regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology 100, 2 (2008), 443–459. DOI:
    [52]
    Duane F. Shell, Jenefer Husman, Jeannine E. Turner, Deborah M. Cliffel, Indira Nath, and Noelle Sweany. 2005. The impact of computer supported collaborative learning communities in high school students’ knowledge building, strategic learning, and perceptions of the classroom. Journal of Educational Computing Research 33, 3 (2005), 327–349. DOI:
    [53]
    Duane F. Shell and Leen-Kiat Soh. 2013. Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology 22, 6 (2013), 899–913. DOI:
    [54]
    Duane F. Shell, Leen-Kiat Soh, Abraham E. Flanigan, and Markeya S. Peteranetz. 2016. Students' initial course motivation and their achievement and retention in college CS1 courses. In Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE’16), New York, NY, USA. The Association for Computing Machinery, 639–644. DOI:
    [55]
    Rebecca L. Shively and Carey S. Ryan. 2013. Longitudinal changes in college math students’ implicit theories of intelligence. Social Psychology of Education 16, 2 (2013), 241–256. DOI:
    [56]
    Beth Simon, Brian Hanks, Laurie Murphy, Sue Fitzgerald, Renee McCauley, Lynda Thomas, and Carol Zander. 2008. Saying isn't necessarily believing: Influencing self-theories in computing. In Proceedings of the 4th ACM International Computing Education Research Conference (ICER’08). ACM, 173–184. DOI:
    [57]
    Donggil Song, Hyeonmi Hong, and Eun Young Oh. 2021. Applying computational analysis of novice learners’ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior 120, (2021), Article 106746. DOI:
    [58]
    Glenda S. Stump, Jenefer Husman, and Marcia Corby. 2014. Engineering students' intelligence beliefs and learning. Journal of Engineering Education 103, 3 (2014), 369–387. DOI:
    [59]
    Jessica Watkins and Eric Mazur. 2013. Retaining students in science, technology, engineering, and mathematics (STEM) majors. Journal of College Science Teaching 42, 5 (2013), 36–41.
    [60]
    Bernard Weiner. 1979. A theory of motivation for some classroom experiences. Journal of Educational Psychology 71, 1 (1979), 3–25. DOI:
    [61]
    Bernard Weiner. 1985. An attributional theory of achievement motivation and emotion. Psychological Review 92 (1985), 548–573. DOI:
    [62]
    David S. Yeager and Carol S. Dweck. 2012. Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist 47, 4 (2012). 302–314. DOI:
    [63]
    Barry J. Zimmerman. 1986. Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology 11, 4 (1986), 307–313. DOI:
    [64]
    Barry J. Zimmerman. 1990. Self-regulated learning and academic achievement: An overview. Educational Psychologist 25, 1 (1990), 3–17. DOI:
    [65]
    Barry J. Zimmerman. 2000. Attaining self-regulation: A social cognitive perspective. In Handbook of Self-Regulation. Academic Press. San Diego, CA, USA, 13–39.
    [66]
    Barry J. Zimmerman. 2002. Becoming a self-regulated learner: An overview. Theory into Practice 41, 2 (2002), 64–70. DOI:
    [67]
    Barry J. Zimmerman and Anastasia Kitsantas. 2014. Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology 39, 2 (2014), 145–155. DOI:
    [68]
    Barry J. Zimmerman and Manuel Martinez Pons. 1986. Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal 23, 4 (1986), 614–628. DOI:
    [69]
    Barry J. Zimmerman, Adam Moylan, John Hudesman, Niesha White, and Bert Flugman. 2011. Enhancing self-reflection and mathematics achievement of at-risk urban technical college students. Psychological Test and Assessment Modeling 53, 1 (2011), 141–160.

    Cited By

    View all
    • (2024)What Learning Strategies are Used by Programming Students? A Qualitative Study Grounded on the Self-regulation of Learning TheoryACM Transactions on Computing Education10.1145/363572024:1(1-26)Online publication date: 19-Feb-2024

    Index Terms

    1. Relationship Between Implicit Intelligence Beliefs and Maladaptive Self-Regulation of Learning

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Computing Education
      ACM Transactions on Computing Education  Volume 23, Issue 3
      September 2023
      233 pages
      EISSN:1946-6226
      DOI:10.1145/3605196
      • Editor:
      • Amy J. Ko
      Issue’s Table of Contents

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 June 2023
      Online AM: 03 May 2023
      Accepted: 31 March 2023
      Revised: 27 February 2023
      Received: 29 June 2022
      Published in TOCE Volume 23, Issue 3

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Academic motivation
      2. implicit intelligence beliefs
      3. undergraduates

      Qualifiers

      • Research-article

      Funding Sources

      • National Science Foundation

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)254
      • Downloads (Last 6 weeks)22

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)What Learning Strategies are Used by Programming Students? A Qualitative Study Grounded on the Self-regulation of Learning TheoryACM Transactions on Computing Education10.1145/363572024:1(1-26)Online publication date: 19-Feb-2024

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      Full Text

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media