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CS0 vs. CS1:: Understanding Fears and Confidence amongst Non-majors in Introductory CS Courses

Published: 03 March 2023 Publication History

Abstract

Previous research has been devoted to improving the experience of non-majors in introductory CS courses. In this study, we compare the experiences of non-majors in two different introductory CS courses, specifically with respect to fears about taking the course and change in confidence levels. CS0 is a computing course intentionally designed for non-majors, and CS1 is a more traditional introductory computing course. Both of these courses were composed primarily of non-majors and were taught by the same instructor. Survey data was collected from 124 students enrolled in CS0, and 502 students enrolled in CS1. Through qualitative analysis, we found that the fears of non-major students entering both of these introductory CS courses fell into one or more of nine distinct categories (e.g., Coding, Perceiving STEM as Difficult, Managing Workload). Additionally, using students' confidence levels at the beginning and end of the courses, we found that students in CS0 had a greater increase in confidence level than those in CS1. Finally, we explored connections between students' fears and how their confidence changed by the end of the course. We found that students across both courses with fears related to coding, lack of preparation, and being left behind had the highest average increase in confidence levels.

References

[1]
Jessica Q. Dawson, Meghan Allen, Alice Campbell, and Anasazi Valair. 2018. Designing an Introductory Programming Course to Improve Non-Majors' Experiences. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. ACM, Baltimore Maryland USA, 26--31. https://doi.org/10.1145/3159450.3159548
[2]
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. ACM, Toronto ON Canada, 21--29. https://doi.org/10.1145/3291279.3339413
[3]
Jamie Gorson and Eleanor O'Rourke. 2020. Why do CS1 Students Think They're Bad at Programming?: Investigating Self-efficacy and Self-assessments at Three Universities. In Proceedings of the 2020 ACM Conference on International Computing Education Research. Virtual Event New Zealand, 170--181. https://doi.org/10.1145/3372782.3406273
[4]
Mark Guzdial and Andrea Forte. 2005. Design process for a non-majors computing course. ACM SIGCSE Bulletin, Vol. 37, 1 (Feb. 2005), 361--365. https://doi.org/10.1145/1047124.1047468
[5]
Brian Harrington, Shichong Peng, Xiaomeng Jin, and Minhaz Khan. 2018. Gender, confidence, and mark prediction in CS examinations. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. ACM, Larnaca Cyprus, 230--235. https://doi.org/10.1145/3197091.3197116
[6]
Lilly Irani. 2004. Understanding gender and confidence in CS course culture. In Proceedings of the 35th SIGCSE technical symposium on Computer science education (SIGCSE '04). ACM, New York, NY, USA, 195--199. https://doi.org/10.1145/971300.971371
[7]
Maria Kallia and Sue Sentance. 2019. Learning to use Functions: The Relationship Between Misconceptions and Self-Efficacy. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. ACM, Minneapolis MN USA, 752--758. https://doi.org/10.1145/3287324.3287377
[8]
Päivi Kinnunen and Beth Simon. 2011. CS majors' self-efficacy perceptions in CS1: results in light of social cognitive theory. In Proceedings of the seventh international workshop on Computing education research (ICER '11). ACM, New York, NY, USA, 19--26. https://doi.org/10.1145/2016911.2016917
[9]
Robert W. Lent, Steven D. Brown, and Kevin C. Larkin. 1986. Self-efficacy in the prediction of academic performance and perceived career options. Journal of Counseling Psychology, Vol. 33, 3 (1986), 265--269. https://doi.org/10.1037/0022-0167.33.3.265 Place: US Publisher: APA.
[10]
Alex Lishinski, Sarah Narvaiz, and Joshua M. Rosenberg. 2022. Self-efficacy, Interest, and Belongingness -- URM Students' Momentary Experiences in CS1. In Proceedings of the 2022 ACM Conference on International Computing Education Research V. 1. ACM, Lugano and Virtual Event Switzerland, 44--60. https://doi.org/10.1145/3501385.3543958
[11]
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. ACM, Melbourne VIC Australia, 211--220. https://doi.org/10.1145/2960310.2960329
[12]
Krista Nelson, Danielle Newman, and Janelle McDaniel. 2013. Gender Differences in Fear of Failure amongst Engineering Students. International Journal of Humanities and Social Science, Vol. 3 (Aug. 2013), 10--18.
[13]
An Nguyen and Colleen M. Lewis. 2020. Competitive Enrollment Policies in Computing Departments Negatively Predict First-Year Students' Sense of Belonging, Self-Efficacy, and Perception of Department. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education. ACM, Portland OR USA, 685--691. https://doi.org/10.1145/3328778.3366805
[14]
Markeya S. Peteranetz, Shiyuan Wang, Duane F. Shell, Abraham E. Flanigan, and Leen-Kiat Soh. 2018. Examining the Impact of Computational Creativity Exercises on College Computer Science Students' Learning, Achievement, Self-Efficacy, and Creativity. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. ACM, Baltimore Maryland USA, 155--160. https://doi.org/10.1145/3159450.3159459
[15]
Vennila Ramalingam, Deborah LaBelle, and Susan Wiedenbeck. 2004. Self-efficacy and mental models in learning to program. In Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education (ITiCSE '04). ACM, New York, NY, USA, 171--175. https://doi.org/10.1145/1007996.1008042
[16]
Adrian Salguero, William G. Griswold, Christine Alvarado, and Leo Porter. 2021. Understanding Sources of Student Struggle in Early Computer Science Courses. In Proceedings of the 17th ACM Conference on International Computing Education Research. ACM, Virtual Event USA, 319--333. https://doi.org/10.1145/3446871.3469755
[17]
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. ACM, Seattle Washington USA, 513--518. https://doi.org/10.1145/3017680.3017781
[18]
Sadia Sharmin. 2020. Open-Ended Exercises in CS1: The Impact on Female, Non-Major and Inexperienced Computer Science Students. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. ACM, Trondheim Norway, 558--558. https://doi.org/10.1145/3341525.3393970
[19]
Mark Urban-Lurain and Donald J. Weinshank. 1999. "I do and I understand": mastery model learning for a large non-major course. In The proceedings of the thirtieth SIGCSE technical symposium on Computer science education (SIGCSE '99). ACM, New York, NY, USA, 150--154. https://doi.org/10.1145/299649.299738
[20]
Susan Wiedenbeck. 2005. Factors affecting the success of non-majors in learning to program. In Proceedings of the 2005 international workshop on Computing education research - ICER '05. ACM Press, Seattle, WA, USA, 13--24. https://doi.org/10.1145/1089786.1089788
[21]
Mark Zarb, Bedour Alshaigy, Dennis Bouvier, Richard Glassey, Janet Hughes, and Charles Riedesel. 2018. An international investigation into student concerns regarding transition into higher education computing. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. ACM, Larnaca Cyprus, 107--129. https://doi.org/10.1145/3293881.3295780
[22]
Daniel Zingaro. 2014. Peer instruction contributes to self-efficacy in CS1. In Proceedings of the 45th ACM technical symposium on Computer science education. ACM, Atlanta Georgia USA, 373--378. https://doi.org/10.1145/2538862.2538878showDOItem

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  • (2024)Exploring Relations between Programming Learning Trajectories and Students' MajorsProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674497(177-180)Online publication date: 5-Jul-2024
  • (2024)Exploring the Acceptance and Effectiveness of Parsons Problems on Scaffolding CS1 RetakersProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653590(681-687)Online publication date: 3-Jul-2024
  • (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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    cover image ACM Conferences
    SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1
    March 2023
    1481 pages
    ISBN:9781450394314
    DOI:10.1145/3545945
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    Published: 03 March 2023

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    Author Tags

    1. computing education research
    2. confidence
    3. cs0
    4. cs1
    5. fears
    6. non-majors

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    • (2024)Exploring Relations between Programming Learning Trajectories and Students' MajorsProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674497(177-180)Online publication date: 5-Jul-2024
    • (2024)Exploring the Acceptance and Effectiveness of Parsons Problems on Scaffolding CS1 RetakersProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653590(681-687)Online publication date: 3-Jul-2024
    • (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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