Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Teaching the Bubble Sort Algorithm Using CS Unplugged Activities at the K-12 Level

Published: 24 January 2025 Publication History

Abstract

Computer Science (CS) Unplugged activities are designed to engage students with CS concepts. It is an active learning approach combining physical interaction with visual representation. This research article investigates the impact of CS Unplugged on students’ understanding of the bubble sort algorithm. Algorithm visualization, traditionally employed for active knowledge construction, serves as the foundation for this approach. The research was conducted as a quasi-experiment in middle and high schools in Split, Croatia, among 204 students. We divided the participants into two groups: experimental (CS Unplugged) and control (traditional teaching). The study included pre-test, mid-test, and post-test assessments. While CS Unplugged did not significantly affect students’ understanding of bubble sort, it positively influenced long-term retention of the algorithm. These results highlight the potential of CS Unplugged activities as an effective teaching approach for CS concepts, specifically in promoting long-term retention of the bubble sort algorithm. The study also revealed misconceptions that included restarting comparisons after each swap, assuming the largest element reaches the end after one swap, and repeatedly swapping the largest element with its neighbor. Addressing these misconceptions through active learning activities like CS Unplugged can contribute to deeper understanding beyond memorization.

References

[1]
Finn Eivind Aasen. 2022. Visualization Tools in Introductory Programming Education. Master Thesis. Department of Informatics Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo.
[2]
Owen Astrachan. 2003. Bubble sort: An archaeological algorithmic analysis. ACM SIGCSE Bull. 35, 1 (January 2003), 1–5. DOI:
[3]
Tim Bell and Jan Vahrenhold. 2018. CS Unplugged—How is it used, and does it work? In Adventures Between Lower Bounds and Higher Altitudes: Essays Dedicated to Juraj Hromkovič on the Occasion of His 60th Birthday. Hans-Joachim Böckenhauer, Dennis Komm, and Walter Unger (Eds.), Springer International Publishing, Cham, 497–521. DOI:
[4]
Peter Bellstrom and Claes Thoren. 2009. Learning how to program through visualization: A pilot study on the bubble sort algorithm. In Proceedings of the 2009 2nd International Conference on the Applications of Digital Information and Web Technologies, 90–94. DOI:
[5]
Péter Bernát. 2014. The methods and goals of teaching sorting algorithms in public education. Acta Didact. Napoc. 7, 2 (2014), 1–9.
[6]
Benjamin S. Bloom, Max D. Engelhart, Edward J. Furst, Walker H. Hill, and David R. Krathwohl. 1956. Taxonomy of Educational Objectives: Handbook I: Cognitive Domain. Longmans, Green & Co.
[7]
Marina Blumenkrants, Hilla Starovisky, and Ariel Shamir. 2006. Narrative algorithm visualization. In Proceedings of the 2006 ACM Symposium on Software Visualization (SoftVis ’06). ACM, New York, NY, 17–26. DOI:
[8]
J. D. Bransford, A. L. Brown, and Rodney R. Cocking. 2000. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. National Academies Press, Washington, D.C. DOI:
[9]
Jerome Seymour Bruner. 1966. Toward a Theory of Instruction. Harvard University Press, London.
[10]
Ibrahim Cetin and Christine Andrews-Larson. 2016. Learning sorting algorithms through visualization construction. Comput. Sci. Educ. 26, 1 (January 2016), 27–43. DOI:
[11]
Louis Cohen, Lawrence Manion, and Keith Morrison. 2011. Research Methods in Education (th ed.). Routledge, London. DOI:
[12]
Kathryn Cunningham, Sarah Blanchard, Barbara Ericson, and Mark Guzdial. 2017. Using tracing and sketching to solve programming problems: Replicating and extending an analysis of what students draw. In Proceedings of the 2017 ACM Conference on International Computing Education Research (ICER ’17). ACM, New York, NY, 164–172. DOI:
[13]
Wanda Dann and Stephen Cooper. 2009. Alice 3: Concrete to abstract. Commun. ACM 52, 8 (August 2009), 27–29. DOI:
[14]
Javier Del Olmo-Muñoz, Ramón Cózar-Gutiérrez, and José Antonio González-Calero. 2020. Computational thinking through unplugged activities in early years of primary education. Comput. Educ. 150 (June 2020), 103832. DOI:
[15]
Yvon Feaster, Luke Segars, Sally K. Wahba, and Jason O. Hallstrom. 2011. Teaching CS Unplugged in the high school (with limited success). In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education (ITiCSE ’11). ACM, New York, NY, 248–252. DOI:
[16]
Scott Grissom, Myles F. McNally, and Tom Naps. 2003. Algorithm visualization in CS education: Comparing levels of student engagement. In Proceedings of the 2003 ACM Symposium on Software Visualization (SoftVis ’03). ACM, New York, NY, 87–94. DOI:
[17]
Shuchi Grover, Patrik Lundh, and Nicholas Jackiw. 2019. Non-programming activities for engagement with foundational concepts in introductory programming. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE ’19). ACM, New York, NY, 1136–1142. DOI:
[18]
Felienne Hermans and Efthimia Aivaloglou. 2017. To Scratch or not to scratch? A controlled experiment comparing plugged first and unplugged first programming lessons. In Proceedings of the 12th Workshop on Primary and Secondary Computing Education. ACM, Nijmegen Netherlands, 49–56. DOI:
[19]
Christopher D. Hundhausen, Sarah A. Douglas, and John T. Stasko. 2002. A meta-study of algorithm visualization effectiveness. J. Vis. Lang. Comput. 13 (2002), 259–290.
[20]
Duane J. Jarc, Michael B. Feldman, and Rachelle S. Heller. 2000. Assessing the benefits of interactive prediction using Web-based algorithm animation courseware. ACM SIGCSE Bull. 32, 1 (March 2000), 377–381. DOI:
[21]
Herman Koppelman and Betsy van Dijk. 2010. Teaching abstraction in introductory courses. In Proceedings of the 15th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ’10). ACM, New York, NY, 174–178. DOI:
[22]
Ari Korhonen. 2003. Visual Algorithm Simulation. Helsinki University of Technology.
[23]
Jeff Lehman. 2009. Computer Science unplugged: K-12 special session. J. Comput. Sci. Coll. 25, 1 (October 2009), 110.
[24]
Joan M. Lucas. 2009. K-6 outreach using “Computer Science Unplugged.” J. Comput. Sci. Coll. 24, 6 (June 2009), 62–63.
[25]
Anne Meier, Hans Spada, and Nikol Rummel. 2007. A rating scheme for assessing the quality of computer-supported collaboration processes. Int. J. Comput.-Support. Collab. Learn. 2, 1 (March 2007), 63–86. DOI:
[26]
Ministry of Science and Education of the Republic of Croatia. 2018. The Informatics Curriculum for Middle and High Schools. Zagreb.
[27]
Monika Mladenović, Žana Žanko, and Marin Aglić Čuvić. 2020. The impact of using program visualization techniques on learning basic programming concepts at the K-12 level. Comput. Appl. Eng. Educ. 29, 1 (August 2020), cae.22315. DOI:
[28]
Briana B. Morrison, Brian Dorn, and Michelle Friend. 2019. Computational thinking bins: Outreach and more. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE ’19). ACM, New York, NY, 1018–1024. DOI:
[29]
Niko Myller, Roman Bednarik, Erkki Sutinen, and Mordechai Ben-Ari. 2009. Extending the engagement taxonomy: Software visualization and collaborative learning. ACM Trans. Comput. Educ. 9, 1 (March 2009), 7:1–7:27. DOI:
[30]
Niko Myller, Mikko Laakso, and Ari Korhonen. 2007. Analyzing engagement taxonomy in collaborative algorithm visualization. ACM SIGCSE Bull. 39 (2007), 251–255.
[31]
Thomas Naps, Stephen Cooper, Boris Koldehofe, Charles Leska, Guido Rößling, Wanda Dann, Ari Korhonen, Lauri Malmi, Jarmo Rantakokko, Rockford J. Ross, et al. 2003. Evaluating the educational impact of visualization. ACM SIGCSE Bull. 35, 4 (2003), 124–136. DOI:
[32]
Thomas L. Naps, Guido Rößling, Vicki Almstrum, Wanda Dann, Rudolf Fleischer, Chris Hundhausen, Ari Korhonen, Lauri Malmi, Myles McNally, Susan Rodger, et al. 2002. Exploring the role of visualization and engagement in computer science education. In Proceedings of the Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (ITiCSE-WGR ’02). ACM, New York, NY, 131–152. DOI:
[33]
Donald A. Norman. 1986. Cognitive engineering. User Centered Syst. Des. 31, 61 (1986), 2.
[34]
Jean Piaget. 1952. The Origins of Intelligence in Children. W W Norton & Co, New York, NY. DOI:
[35]
Anthony Robins, Janet Rountree, and Nathan Rountree. 2003. Learning and teaching programming: A review and discussion. Comput. Sci. Educ. 13, 2 (2003), 137–172. DOI:
[36]
Juha Sorva, Ville Karavirta, and Lauri Malmi. 2013. A review of generic program visualization systems for introductory programming education. ACM Trans. Comput. Educ. 13, 4 (2013), 1–64. DOI:
[37]
David Statter and Michal Armoni. 2017. Learning abstraction in computer science: A gender perspective. In Proceedings of the 12th Workshop on Primary and Secondary Computing Education (WiPSCE ’17). ACM, New York, NY, 5–14. DOI:
[38]
David Statter and Michal Armoni. 2020. Teaching abstraction in computer science to 7th grade students. ACM Trans. Comput. Educ. 20, 1 (January 2020), 1–37. DOI:
[39]
Lihui Sun, Linlin Hu, and Danhua Zhou. 2021. Which way of design programming activities is more effective to promote K-12 students’ computational thinking skills? A meta-analysis. J. Comput. Assist. Learn. 37, 4 (2021), 1048–1062. DOI:
[40]
Rivka Taub, Michal Armoni, and Mordechai Ben-Ari. 2012. CS Unplugged and middle-school students’ views, attitudes, and intentions regarding CS. ACM Trans. Comput. Educ. 12, 2 (April 2012), 8:1–8:29. DOI:
[41]
Renate Thies and Jan Vahrenhold. 2012. Reflections on outreach programs in CS classes: Learning objectives for “unplugged” activities. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (SIGCSE ’12). ACM, New York, NY, 487–492. DOI:
[42]
Cecile Yehezkel, Mordechai Ben-Ari, and Tommy Dreyfus. 2005. Computer architecture and mental models. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’05). ACM, New York, NY, 101–105. DOI:
[43]
Bubble Sort Algorithm (in Croatian). 2022. Split. Retrieved from https://www.youtube.com/watch?v=nCI9x2gFmu0

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Computing Education
ACM Transactions on Computing Education  Volume 25, Issue 1
March 2025
76 pages
EISSN:1946-6226
DOI:10.1145/3703020
  • Editor:
  • Amy J. Ko
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 January 2025
Online AM: 28 November 2024
Accepted: 24 October 2024
Revised: 17 October 2024
Received: 30 June 2023
Published in TOCE Volume 25, Issue 1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. visualization
  2. CS Unplugged
  3. bubble sort
  4. computer science education
  5. misconceptions

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 146
    Total Downloads
  • Downloads (Last 12 months)146
  • Downloads (Last 6 weeks)68
Reflects downloads up to 03 Feb 2025

Other Metrics

Citations

View Options

Login options

Full Access

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

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media