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Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-Majors

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Eleanor O'RourkeAuthors Info & Claims

ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1

Pages 27 - 41

https://doi.org/10.1145/3632620.3671119

Published: 12 August 2024 Publication History

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ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1

Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-Majors

Pages 27 - 41

Abstract
References

Abstract

Introductory computer science for non-majors, often referred to as CS0, is a course that is designed to be more accessible and less intimidating than CS1, with the goal of alleviating barriers and fears associated with learning computer science (CS). However, despite this intention, many students still struggle in CS0 and these courses do not always successfully prepare students for future CS learning experiences. In this paper, we study the experiences of CS0 students with a particular focus on the intersection of their metacognition, affect, and behaviors. To study students’ daily learning experiences, we collected data from 20 participants who completed structured daily diaries and retrospective interviews over the course of a single homework assignment. Through a thematic analysis of the diaries and interviews, we identified three distinct patterns of engagement that highlight the importance of metacognitive knowledge of strategies, or a students’ understanding of when, why, and how to effectively use regulation and disciplinary strategies while working on tasks. The three patterns of engagement include: (1) avoidance behaviors resulting from negative emotions, negative judgements, and a lack of metacognitive knowledge of strategies, (2) persistence or re-engagement behaviors despite negative emotions and judgements aided by metacognitive knowledge of strategies, and (3) persistence behaviors with evidence that metacognitive knowledge of strategies prevented students from forming negative judgements in the first place. We contribute an initial model of the interplay of metacognition, affect, and behaviors in CS learning, showing the role of metacognitive knowledge of strategies in helping students persist in the face of struggle. In our discussion, we advocate for explicit interventions that support students in developing metacognitive knowledge of strategies while also supporting their sometimes challenging emotional experiences.

References

[1]

Noura Albarakati, Lisa DiPippo, and Victor Fay-Wolfe. 2021. Rethinking CS0 to improve performance and Retention. Proceedings of the 23rd Australasian Computing Education Conference (Feb 2021). https://doi.org/10.1145/3441636.3442314

Digital Library

[2]

Noura Albarakati, Lisa DiPippo, and Victor Fay-Wolfe. 2021. Rethinking CS0 to improve performance and Retention. Proceedings of the 23rd Australasian Computing Education Conference (Feb 2021). https://doi.org/10.1145/3441636.3442314

Digital Library

[3]

Susan A. Ambrose. 2010. How learning works: Seven research-based principles for smart teaching.

[4]

Susan Bergin, Ronan Reilly, and Desmond Traynor. 2005. Examining the role of self-regulated learning on Introductory programming performance. Proceedings of the 2005 international workshop on Computing education research - ICER ’05 (2005). https://doi.org/10.1145/1089786.1089794

Digital Library

[5]

Monique Boekaerts. 1997. Self-Regulated Learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction 7, 2 (Jun 1997), 161–186. https://doi.org/10.1016/s0959-4752(96)00015-1

[6]

Ann L. Brown. 1978. Knowing when, where, and how to remember: A problem of metacognition. Eric Reports.

[7]

Michelene T.H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann, and Robert Glaser. 1989. Self‐explanations: How students study and use examples in learning to solve problems. Cognitive Science 13, 2 (Apr 1989), 145–182. https://doi.org/10.1207/s15516709cog1302_1

[8]

Lyn Corno. 1986. The metacognitive control components of self-regulated learning. Contemporary Educational Psychology 11, 4 (Oct 1986), 333–346. https://doi.org/10.1016/0361-476x(86)90029-9

[9]

Thomas J. Cortina. 2007. An introduction to computer science for non-majors using principles of computation. Proceedings of the 38th SIGCSE technical symposium on Computer science education (Mar 2007). https://doi.org/10.1145/1227310.1227387

Digital Library

[10]

Jessica Q. Dawson, Meghan Allen, Alice Campbell, and Anasazi Valair. 2018. Designing an introductory programming course to improve Non-Majors’ experiences. Proceedings of the 49th ACM Technical Symposium on Computer Science Education (Feb 2018). https://doi.org/10.1145/3159450.3159548

Digital Library

[11]

Hester de Boer, Anouk S. Donker, Danny D.N.M. Kostons, and Greetje P.C. van der Werf. 2018. Long-term effects of metacognitive strategy instruction on student academic performance: A meta-analysis. Educational Research Review 24 (Jun 2018), 98–115. https://doi.org/10.1016/j.edurev.2018.03.002

[12]

Carol S. Dweck. 1999. Self-theories: Their role in motivation, personality, and development. Psychology Press.

[13]

Carol S. Dweck. 2006. Mindset: the new psychology of Success. Random House.

[14]

Anastasia Efklides. 2001. Metacognitive experiences in problem solving. Trends and Prospects in Motivation Research (2001), 297–323. https://doi.org/10.1007/0-306-47676-2_16

[15]

Anastasia Efklides. 2006. Metacognition and affect: What can metacognitive experiences tell us about the learning process?Educational Research Review 1, 1 (Jan 2006), 3–14. https://doi.org/10.1016/j.edurev.2005.11.001

[16]

Anastasia Efklides. 2011. Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist 46, 1 (Jan 2011), 6–25. https://doi.org/10.1080/00461520.2011.538645

[17]

Anneli Eteläpelto. 1993. Metacognition and the expertise of computer program comprehension. Scandinavian Journal of Educational Research 37, 3 (Jan 1993), 243–254. https://doi.org/10.1080/0031383930370305

[18]

Katrina Falkner, Rebecca Vivian, and Nickolas J.G. Falkner. 2014. Identifying computer science self-regulated learning strategies. Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE ’14 (2014). https://doi.org/10.1145/2591708.2591715

Digital Library

[19]

Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, and Shriram Krishnamurthi. 2004. The teachscheme! project: Computing and programming for every student. Computer Science Education 14, 1 (Jan 2004), 55–77. https://doi.org/10.1076/csed.14.1.55.23499

[20]

John H. Flavell. 1979. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry.American Psychologist 34, 10 (1979), 906–911. https://doi.org/10.1037//0003-066x.34.10.906

[21]

Joseph P Forgas. 1994. The role of Emotion in Social Judgments: An introductory review and an affect infusion model (AIM). European Journal of Social Psychology 24, 1 (Jan 1994), 1–24. https://doi.org/10.1002/ejsp.2420240102

[22]

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 (Toronto ON, Canada) (ICER ’19). Association for Computing Machinery, New York, NY, USA, 21–29. https://doi.org/10.1145/3291279.3339413

Digital Library

[23]

Jamie Gorson and Eleanor O’Rourke. 2020. Why do CS1 students think they’re bad at programming?Proceedings of the 2020 ACM Conference on International Computing Education Research (Aug 2020). https://doi.org/10.1145/3372782.3406273

Digital Library

[24]

Lin Guo. 2022. Using metacognitive prompts to enhance self‐regulated learning and learning outcomes: A meta‐analysis of experimental studies in computer‐based Learning Environments. Journal of Computer Assisted Learning 38, 3 (Feb 2022), 811–832. https://doi.org/10.1111/jcal.12650

[25]

Mark Guzdial and Andrea Forte. 2005. Design process for a non-majors computing course. Proceedings of the 36th SIGCSE technical symposium on Computer science education (Feb 2005). https://doi.org/10.1145/1047344.1047468

Digital Library

[26]

Emma Hogan, Ruoxuan Li, and Adalbert Gerald Soosai Raj. 2023. CS0 vs. CS1: Understanding Fears and Confidence amongst Non-majors in Introductory CS Courses. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 25–31. https://doi.org/10.1145/3545945.3569865

Digital Library

[27]

Sorva Juha. 2013. Notional machines and introductory programming education. ACM Trans. Comput. Educ 13, 2 (2013), 1–31.

Digital Library

[28]

Judy Kay, Michael Barg, Alan Fekete, Tony Greening, Owen Hollands, Jeffrey H. Kingston, and Kate Crawford. 2000. Problem-based learning for foundation computer science courses. Computer Science Education 10, 2 (Aug 2000), 109–128. https://doi.org/10.1076/0899-3408(200008)10:2;1-c;ft109

[29]

Michelle E. Kiger and Lara Varpio. 2020. Thematic analysis of qualitative data: Amee Guide no. 131. Medical Teacher 42, 8 (May 2020), 846–854. https://doi.org/10.1080/0142159x.2020.1755030

[30]

Paivi Kinnunen and Beth Simon. 2010. Experiencing programming assignments in CS1. Proceedings of the Sixth international workshop on Computing education research (Aug 2010). https://doi.org/10.1145/1839594.1839609

Digital Library

[31]

Päivi Kinnunen and Beth Simon. 2012. My program is ok – am I? computing freshmen’s experiences of doing programming assignments. Computer Science Education 22, 1 (Mar 2012), 1–28. https://doi.org/10.1080/08993408.2012.655091

[32]

Asher Koriat and Morris Goldsmith. 1996. Monitoring and control processes in the strategic regulation of memory accuracy.Psychological Review 103, 3 (1996), 490–517. https://doi.org/10.1037//0033-295x.103.3.490

[33]

Deanna Kuhn. 2000. Metacognitive development. Current Directions in Psychological Science 9, 5 (Oct 2000), 178–181. https://doi.org/10.1111/1467-8721.00088

[34]

Harrison Kwik, Haoqi Zhang, and Eleanor O’Rourke. 2022. How Do Students Seek Help and How Do TAs Respond?: Investigating Help-Seeking Strategies in CS1 Office Hours. In SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education, Providence, RI, USA, March 3-5, 2022, Volume 2, Larry Merkle, Maureen Doyle, Judithe Sheard, Leen-Kiat Soh, and Brian Dorn (Eds.). ACM, 1130. https://doi.org/10.1145/3478432.3499099

Digital Library

[35]

Kathleen J. Lehman, Julia Rose Karpicz, Veronika Rozhenkova, Jamelia Harris, and Tomoko M. Nakajima. 2021. Growing Enrollments Require Us to Do More: Perspectives on Broadening Participation During an Undergraduate Computing Enrollment Boom. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (Virtual Event, USA) (SIGCSE ’21). Association for Computing Machinery, New York, NY, USA, 809–815. https://doi.org/10.1145/3408877.3432370

Digital Library

[36]

Paul Luo Li, Amy J. Ko, and Andrew Begel. 2019. What distinguishes Great Software Engineers?Empirical Software Engineering 25, 1 (Dec 2019), 322–352. https://doi.org/10.1007/s10664-019-09773-y

Digital Library

[37]

Yinmiao Li, Haoqi Zhang, and Eleanor O’Rourke. 2024. The Undervalued Disciplinary and Emotional Support Provided By Teaching Assistants in Introductory Computer Science Courses. International Society of the Learning Sciences (2024), 1498–1501.

[38]

Alex Lishinski and Joshua Rosenberg. 2021. All the pieces matter: The relationship of momentary self-efficacy and affective experiences with CS1 achievement and interest in computing. Proceedings of the 17th ACM Conference on International Computing Education Research (Aug 2021). https://doi.org/10.1145/3446871.3469740

Digital Library

[39]

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. Proceedings of the 2016 ACM Conference on International Computing Education Research (Aug 2016). https://doi.org/10.1145/2960310.2960329

Digital Library

[40]

Dastyni Loksa, Amy J. Ko, Will Jernigan, Alannah Oleson, Christopher J. Mendez, and Margaret M. Burnett. 2016. Programming, problem solving, and self-awareness. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (May 2016). https://doi.org/10.1145/2858036.2858252

Digital Library

[41]

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 (Dec 2022), 1–31. https://doi.org/10.1145/3487050

Digital Library

[42]

Yana Malysheva, John Allen, and Caitlin Kelleher. 2022. How do teaching assistants teach? characterizing the interactions between students and TAS in a computer science course. 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (Sep 2022). https://doi.org/10.1109/vl/hcc53370.2022.9832962

[43]

C. Mcdowell, L. Werner, H.E. Bullock, and J. Fernald. 2003. The impact of pair programming on student performance, perception and persistence. 25th International Conference on Software Engineering, 2003. Proceedings. (2003). https://doi.org/10.1109/icse.2003.1201243

[44]

Rodrigo Pessoa Medeiros, Geber Lisboa Ramalho, and Taciana Pontual Falcao. 2019. A systematic literature review on teaching and learning introductory programming in Higher Education. IEEE Transactions on Education 62, 2 (May 2019), 77–90. https://doi.org/10.1109/te.2018.2864133

Digital Library

[45]

Tilman Michaeli and Ralf Romeike. 2019. Improving Debugging Skills in the Classroom: The Effects of Teaching a Systematic Debugging Process. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education (Glasgow, Scotland, Uk) (WiPSCE ’19). Association for Computing Machinery, New York, NY, USA, Article 15, 7 pages. https://doi.org/10.1145/3361721.3361724

Digital Library

[46]

Thomas O. Nelson. 1990. Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation (1990), 125–173. https://doi.org/10.1016/s0079-7421(08)60053-5

[47]

Sharon Nelson-Le Gall. 1981. Help-seeking: An understudied problem-solving skill in children. Developmental Review 1, 3 (Sep 1981), 224–246. https://doi.org/10.1016/0273-2297(81)90019-8

[48]

Yulia Pechorina, Keith Anderson, and Paul Denny. 2023. Metacodenition: Scaffolding the problem-solving process for novice programmers. Proceedings of the 25th Australasian Computing Education Conference (Jan 2023). https://doi.org/10.1145/3576123.3576130

Digital Library

[49]

Paul R. Pintrich. 2000. The role of goal orientation in self-regulated learning. Handbook of Self-Regulation (2000), 451–502. https://doi.org/10.1016/b978-012109890-2/50043-3

[50]

Paul R. Pintrich, Christopher A. Wolters, and Gail P. Baxter. 2000. Assessing Metacognition and Self-Regulated Learning. Lincoln, NE: Buros Institute of Mental Measurements, 43–97.

[51]

Leo Porter and Beth Simon. 2013. Retaining nearly one-third more majors with a trio of instructional best practices in CS1. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (Denver, Colorado, USA) (SIGCSE ’13). Association for Computing Machinery, New York, NY, USA, 165–170. https://doi.org/10.1145/2445196.2445248

Digital Library

[52]

Christine Reilly, Emmett Tomai, and Laura M. Grabowski. 2015. An evaluation of how changes to the introductory computer science course sequence impact student success. 2015 IEEE Frontiers in Education Conference (FIE) (Oct 2015). https://doi.org/10.1109/fie.2015.7344029

Digital Library

[53]

Linda J. Sax, Kathleen J. Lehman, and Christina Zavala. 2017. Examining the Enrollment Growth: Non-CS Majors in CS1 Courses. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 513–518. https://doi.org/10.1145/3017680.3017781

Digital Library

[54]

Gregory Schraw and David Moshman. 1995. Metacognitive theories. Educational Psychology Review 7, 4 (Dec 1995), 351–371. https://doi.org/10.1007/bf02212307

[55]

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 (Feb 2013), 899–913. https://doi.org/10.1007/s10956-013-9437-9

[56]

Raja Sooriamurthi. 2009. Introducing abstraction and decomposition to novice programmers. SIGCSE Bull. 41, 3 (jul 2009), 196–200. https://doi.org/10.1145/1595496.1562939

Digital Library

[57]

Julie Dangremond Stanton, Amanda J. Sebesta, and John Dunlosky. 2021. Fostering metacognition to support student learning and performance. CBE—Life Sciences Education 20, 2 (Jun 2021). https://doi.org/10.1187/cbe.20-12-0289

[58]

Pina Tarricone. 2011. The taxonomy of Metacognition. Psychology Press.

[59]

Christine Unterhitzenberger and Kate Lawrence. 2022. Diary method in Project Studies. Project Leadership and Society 3 (Dec 2022), 100054. https://doi.org/10.1016/j.plas.2022.100054

[60]

Marcel Veenman and Jan J. Elshout. 1999. Changes in the relation between cognitive and metacognitive skills during the acquisition of expertise. European Journal of Psychology of Education 14, 4 (Dec 1999), 509–523. https://doi.org/10.1007/bf03172976

[61]

Marcel V. Veenman, Bernadette H. Van Hout-Wolters, and Peter Afflerbach. 2006. Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning 1, 1 (Mar 2006), 3–14. https://doi.org/10.1007/s11409-006-6893-0

[62]

J. D. Vermunt. 1996. Metacognitive, cognitive and affective aspects of learning styles and strategies: A phenomenographic analysis. Higher Education 31, 1 (Jan 1996), 25–50. https://doi.org/10.1007/bf00129106

[63]

Margaret C. Wang, Geneva D. Haertel, and Herbert J. Walberg. 1990. What influences learning? A content analysis of review literature. The Journal of Educational Research 84, 1 (Sep 1990), 30–43. https://doi.org/10.1080/00220671.1990.10885988

[64]

Benjamin Xie, Jared Ordona Lim, Paul K.D. Pham, Min Li, and Amy J. Ko. 2023. Developing novice programmers’ self-regulation skills with code replays. Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 (Aug 2023). https://doi.org/10.1145/3568813.3600127

Digital Library

[65]

Lisa Yan, Annie Hu, and Chris Piech. 2019. Pensieve: Feedback on Coding Process for Novices. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Feb 2019). https://doi.org/10.1145/3287324.3287483

Digital Library

[66]

Qing Zhang and Barbara B. Lockee. 2022. Designing a framework to facilitate metacognitive strategy development in computer-mediated problem-solving instruction. Journal of Formative Design in Learning 6, 2 (Aug 2022), 127–143. https://doi.org/10.1007/s41686-022-00068-y

[67]

Stuart Zweben. 2019. Enrollment and retention in U.S. computer science bachelor’s programs in 2016-17. ACM Inroads 10, 4 (Nov 2019), 47–59. https://doi.org/10.1145/3366690

Digital Library

Cited By

Guzdial MNelson-Fromm TStone JYuen TShoop LRebelsky SPrather J(2025)Designing Courses for Liberal Arts and Sciences Students Contextualized around Creative Expression and Social JusticeProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701896(423-429)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701896
Ko SStephens-Martinez K(undefined)Rethinking computing students’ help resource utilization through sequentialityACM Transactions on Computing Education10.1145/3716860
https://dl.acm.org/doi/10.1145/3716860

Index Terms

Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-Majors
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
2. 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

ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1

August 2024

539 pages

ISBN:9798400704758

DOI:10.1145/3632620

Editors:
Paul Denny
University of Auckland, New Zealand
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Leo Porter
University of California San Diego, USA
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Margaret Hamilton
RMIT University, Australia
,
Briana Morrison
University of Virginia, USA

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Cited By

Guzdial MNelson-Fromm TStone JYuen TShoop LRebelsky SPrather J(2025)Designing Courses for Liberal Arts and Sciences Students Contextualized around Creative Expression and Social JusticeProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701896(423-429)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701896
Ko SStephens-Martinez K(undefined)Rethinking computing students’ help resource utilization through sequentialityACM Transactions on Computing Education10.1145/3716860
https://dl.acm.org/doi/10.1145/3716860

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