Document Open Access Logo

Kumon-Inspired Approach to Teaching Programming Fundamentals

Authors Ivone Amorim , Pedro Baltazar Vasconcelos , João Pedro Pedroso

PDF
Thumbnail PDF

File

OASIcs.ICPEC.2024.5.pdf
  • Filesize: 0.7 MB
  • 13 pages

Document Identifiers

Author Details

Ivone Amorim
  • PORTIC - Porto Research, Technology & Innovation Center, Polytechnic of Porto (IPP), Portugal
Pedro Baltazar Vasconcelos
  • LIACC & Department of Computer Science, Faculty of Sciences, University of Porto, Portugal
João Pedro Pedroso
  • CMUP & Department of Computer Science, Faculty of Sciences, University of Porto, Portugal

Funding

  • Amorim, Ivone: Partially supported by CMUP, which is financed by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project with reference UIDB/00144/2020.
  • Vasconcelos, Pedro Baltazar: Partially supported by: Base Funding - UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory - LIACC - funded by national funds through the FCT/MCTES (PIDDAC).
  • Pedroso, João Pedro: Partially supported by CMUP, which is financed by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project with reference UIDB/00144/2020.

Cite As Get BibTex

Ivone Amorim, Pedro Baltazar Vasconcelos, and João Pedro Pedroso. Kumon-Inspired Approach to Teaching Programming Fundamentals. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.ICPEC.2024.5

Abstract

Integration of introductory programming into higher education programs beyond computer science has lead to an increase in the failure and drop out rates of programming courses. In this context, programming instructors have explored new methodologies by introducing dynamic elements in the teaching-learning process, such as automatic code evaluation systems and gamification. Even though these methods have shown to be successful in improving students' engagement, they do not address all the existing problems and new strategies should be explored. In this work, we propose a new approach that combines the strengths of the Kumon method for personalized learning and progressive skill acquisition with the ability of online judge systems to provide automated assessment and immediate feedback. This approach has been used in teaching Programming I to students in several bachelor degrees and led to a 10% increase in exam approval rates compared to the baseline editions in which our Kumon-inspired methodology was not implemented.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Computer science education
  • Applied computing → Interactive learning environments
Keywords
  • Programming teaching
  • Programming education
  • Kumon method
  • Progressive learning
  • Online judge system

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Kirsti M Ala-Mutka. A survey of automated assessment approaches for programming assignments. Computer Science Education, 15(2):83-102, 2005. URL: https://doi.org/10.1080/08993400500150747.
  2. Aldrich Ellis Asuncion, Brian Christopher Guadalupe, and Gerard Francis Ortega. The abc workbook: Adapting online judge systems for introductory programming classes. In Proceedings of the 30th International Conference on Computers in Education, volume 2, pages 395-400. IEEE, 2022. URL: https://icce2022.apsce.net/uploads/P2_W05_052.pdf.
  3. Yorah Bosse, David Redmiles, and Marco A. Gerosa. Pedagogical content for professors of introductory programming courses. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '19, pages 429-435, New York, NY, USA, 2019. Association for Computing Machinery. URL: https://doi.org/10.1145/3304221.3319776.
  4. Chin-Soon Cheah. Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 2020. Google Scholar
  5. Rodrigo Duran, Jan-Mikael Rybicki, Juha Sorva, and Arto Hellas. Exploring the value of student self-evaluation in introductory programming. In Proceedings of the 2019 ACM Conference on International Computing Education Research, ICER '19, pages 121-130, New York, NY, USA, 2019. Association for Computing Machinery. URL: https://doi.org/10.1145/3291279.3339407.
  6. Stephen H. Edwards and Manuel A. Perez-Quinones. Web-cat: automatically grading programming assignments. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '08, page 328, New York, NY, USA, 2008. Association for Computing Machinery. URL: https://doi.org/10.1145/1384271.1384371.
  7. José Figueiredo and Francisco José García-Peñalvo. Strategies to increase success in learning programming. 2022 International Symposium on Computers in Education (SIIE), pages 1-6, 2022. URL: https://api.semanticscholar.org/CorpusID:254911096.
  8. Anabela Gomes and Antonio Mendes. Learning to program - difficulties and solutions. In Proceedings of the International Conference on Engineering Education – ICEE 2007, pages 283-287, January 2007. Google Scholar
  9. Petri Ihantola, Tuukka Ahoniemi, Ville Karavirta, and Otto Seppälä. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli Calling '10, pages 86-93, New York, NY, USA, 2010. Association for Computing Machinery. URL: https://doi.org/10.1145/1930464.1930480.
  10. Mike Joy, Nathan Griffiths, and Russell Boyatt. The boss online submission and assessment system. J. Educ. Resour. Comput., 5(3):2-es, September 2005. URL: https://doi.org/10.1145/1163405.1163407.
  11. Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. Towards a systematic review of automated feedback generation for programming exercises. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '16, pages 41-46, New York, NY, USA, 2016. Association for Computing Machinery. URL: https://doi.org/10.1145/2899415.2899422.
  12. Alain Kabo Mbiada, Bassey Isong, Francis Lugayizi, and Adnan Abu-Mahfouz. Introductory computer programming teaching and learning approaches: Review. In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1-8, 2022. URL: https://doi.org/10.1109/ICECET55527.2022.9873427.
  13. L. Orcos, R. M. Hernández-Carrera, M. J. Espigares, and Á. Alberto Magreñán. The kumon method: Its importance in the improvement on the teaching and learning of mathematics from the first levels of early childhood and primary education. Mathematics, 7(1), 2019. URL: https://doi.org/10.3390/math7010109.
  14. José Carlos Paiva, José Paulo Leal, and Ricardo Queirós. Authoring game-based programming challenges to improve students' motivation. In Michael E. Auer and Thrasyvoulos Tsiatsos, editors, The Challenges of the Digital Transformation in Education, pages 602-613, Cham, 2020. Springer International Publishing. Google Scholar
  15. Mário Pinto and Teresa Terroso. Learning Computer Programming: A Gamified Approach. In Alberto Simões and João Carlos Silva, editors, Third International Computer Programming Education Conference (ICPEC 2022), volume 102 of Open Access Series in Informatics (OASIcs), pages 11:1-11:8, Dagstuhl, Germany, 2022. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/OASIcs.ICPEC.2022.11.
  16. Yizhou Qian and James Lehman. Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Trans. Comput. Educ., 18(1), October 2017. URL: https://doi.org/10.1145/3077618.
  17. Atajan Rovshenov and Fırat Sarsar. Research trends in programming education: A systematic review of the articles published between 2012-2020. Journal of Educational Technology and Online Learning, 6(1):48-81, 2023. URL: https://doi.org/10.31681/jetol.1201010.
  18. Ján Skalka, Martin Drlík, and Juraj Obonya. Automated assessment in learning and teaching programming languages using virtual learning environment. In 2019 IEEE Global Engineering Education Conference (EDUCON), pages 689-697, 2019. URL: https://doi.org/10.1109/EDUCON.2019.8725127.
  19. Jaime Spacco, Paul Denny, Brad Richards, David Babcock, David Hovemeyer, James Moscola, and Robert Duvall. Analyzing student work patterns using programming exercise data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE '15, pages 18-23, New York, NY, USA, 2015. Association for Computing Machinery. URL: https://doi.org/10.1145/2676723.2677297.
  20. Python standard library. Doctest library documentation. URL: https://docs.python.org/3/library/doctest.html.
  21. Nancy Ukai. The kumon approach to teaching and learning. Journal of Japanese Studies, 20:87, 1994. URL: https://api.semanticscholar.org/CorpusID:150129343.
  22. Usmadi, Amelia Agita, and Ergusni. The effect of application kumon learning method in learning mathematics of ability troubleshooting mathematics of students. Journal of Physics: Conference Series, 1429(1):012005, 2020. URL: https://doi.org/10.1088/1742-6596/1429/1/012005.
  23. Pedro Vasconcelos and Rita P. Ribeiro. Using Property-Based Testing to Generate Feedback for C Programming Exercises. In Ricardo Queirós, Filipe Portela, Mário Pinto, and Alberto Simões, editors, First International Computer Programming Education Conference (ICPEC 2020), volume 81 of Open Access Series in Informatics (OASIcs), pages 28:1-28:10, Dagstuhl, Germany, 2020. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/OASIcs.ICPEC.2020.28.
  24. Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, and Tomasz Sternal. A survey on online judge systems and their applications. ACM Comput. Surv., 51(1), January 2018. URL: https://doi.org/10.1145/3143560.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact